<!DOCTYPE html>

<html>

<head>

<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />


<meta name="author" content="sebastian" />

<meta name="date" content="2025-10-15" />

<title>regression_analysis.R</title>

<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
// be compatible with the behavior of Pandoc < 2.8).
document.addEventListener('DOMContentLoaded', function(e) {
  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
  var i, h, a;
  for (i = 0; i < hs.length; i++) {
    h = hs[i];
    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
    a = h.attributes;
    while (a.length > 0) h.removeAttribute(a[0].name);
  }
});
</script>
<script>/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var f="3.6.0",S=function(e,t){return new S.fn.init(e,t)};function p(e){var t=!!e&&"length"in e&&e.length,n=w(e);return!m(e)&&!x(e)&&("array"===n||0===t||"number"==typeof t&&0<t&&t-1 in e)}S.fn=S.prototype={jquery:f,constructor:S,length:0,toArray:function(){return s.call(this)},get:function(e){return null==e?s.call(this):e<0?this[e+this.length]:this[e]},pushStack:function(e){var t=S.merge(this.constructor(),e);return t.prevObject=this,t},each:function(e){return S.each(this,e)},map:function(n){return this.pushStack(S.map(this,function(e,t){return n.call(e,t,e)}))},slice:function(){return this.pushStack(s.apply(this,arguments))},first:function(){return this.eq(0)},last:function(){return this.eq(-1)},even:function(){return this.pushStack(S.grep(this,function(e,t){return(t+1)%2}))},odd:function(){return this.pushStack(S.grep(this,function(e,t){return t%2}))},eq:function(e){var t=this.length,n=+e+(e<0?t:0);return this.pushStack(0<=n&&n<t?[this[n]]:[])},end:function(){return this.prevObject||this.constructor()},push:u,sort:t.sort,splice:t.splice},S.extend=S.fn.extend=function(){var e,t,n,r,i,o,a=arguments[0]||{},s=1,u=arguments.length,l=!1;for("boolean"==typeof a&&(l=a,a=arguments[s]||{},s++),"object"==typeof a||m(a)||(a={}),s===u&&(a=this,s--);s<u;s++)if(null!=(e=arguments[s]))for(t in e)r=e[t],"__proto__"!==t&&a!==r&&(l&&r&&(S.isPlainObject(r)||(i=Array.isArray(r)))?(n=a[t],o=i&&!Array.isArray(n)?[]:i||S.isPlainObject(n)?n:{},i=!1,a[t]=S.extend(l,o,r)):void 0!==r&&(a[t]=r));return a},S.extend({expando:"jQuery"+(f+Math.random()).replace(/\D/g,""),isReady:!0,error:function(e){throw new Error(e)},noop:function(){},isPlainObject:function(e){var t,n;return!(!e||"[object Object]"!==o.call(e))&&(!(t=r(e))||"function"==typeof(n=v.call(t,"constructor")&&t.constructor)&&a.call(n)===l)},isEmptyObject:function(e){var t;for(t in e)return!1;return!0},globalEval:function(e,t,n){b(e,{nonce:t&&t.nonce},n)},each:function(e,t){var n,r=0;if(p(e)){for(n=e.length;r<n;r++)if(!1===t.call(e[r],r,e[r]))break}else for(r in e)if(!1===t.call(e[r],r,e[r]))break;return e},makeArray:function(e,t){var n=t||[];return null!=e&&(p(Object(e))?S.merge(n,"string"==typeof e?[e]:e):u.call(n,e)),n},inArray:function(e,t,n){return null==t?-1:i.call(t,e,n)},merge:function(e,t){for(var n=+t.length,r=0,i=e.length;r<n;r++)e[i++]=t[r];return e.length=i,e},grep:function(e,t,n){for(var r=[],i=0,o=e.length,a=!n;i<o;i++)!t(e[i],i)!==a&&r.push(e[i]);return r},map:function(e,t,n){var r,i,o=0,a=[];if(p(e))for(r=e.length;o<r;o++)null!=(i=t(e[o],o,n))&&a.push(i);else for(o in e)null!=(i=t(e[o],o,n))&&a.push(i);return g(a)},guid:1,support:y}),"function"==typeof Symbol&&(S.fn[Symbol.iterator]=t[Symbol.iterator]),S.each("Boolean Number String Function Array Date RegExp Object Error Symbol".split(" "),function(e,t){n["[object "+t+"]"]=t.toLowerCase()});var d=function(n){var e,d,b,o,i,h,f,g,w,u,l,T,C,a,E,v,s,c,y,S="sizzle"+1*new Date,p=n.document,k=0,r=0,m=ue(),x=ue(),A=ue(),N=ue(),j=function(e,t){return e===t&&(l=!0),0},D={}.hasOwnProperty,t=[],q=t.pop,L=t.push,H=t.push,O=t.slice,P=function(e,t){for(var n=0,r=e.length;n<r;n++)if(e[n]===t)return n;return-1},R="checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",M="[\\x20\\t\\r\\n\\f]",I="(?:\\\\[\\da-fA-F]{1,6}"+M+"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",W="\\["+M+"*("+I+")(?:"+M+"*([*^$|!~]?=)"+M+"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|("+I+"))|)"+M+"*\\]",F=":("+I+")(?:\\((('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|((?:\\\\.|[^\\\\()[\\]]|"+W+")*)|.*)\\)|)",B=new RegExp(M+"+","g"),$=new RegExp("^"+M+"+|((?:^|[^\\\\])(?:\\\\.)*)"+M+"+$","g"),_=new RegExp("^"+M+"*,"+M+"*"),z=new RegExp("^"+M+"*([>+~]|"+M+")"+M+"*"),U=new RegExp(M+"|>"),X=new RegExp(F),V=new RegExp("^"+I+"$"),G={ID:new RegExp("^#("+I+")"),CLASS:new RegExp("^\\.("+I+")"),TAG:new RegExp("^("+I+"|[*])"),ATTR:new RegExp("^"+W),PSEUDO:new RegExp("^"+F),CHILD:new RegExp("^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\("+M+"*(even|odd|(([+-]|)(\\d*)n|)"+M+"*(?:([+-]|)"+M+"*(\\d+)|))"+M+"*\\)|)","i"),bool:new RegExp("^(?:"+R+")$","i"),needsContext:new RegExp("^"+M+"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\("+M+"*((?:-\\d)?\\d*)"+M+"*\\)|)(?=[^-]|$)","i")},Y=/HTML$/i,Q=/^(?:input|select|textarea|button)$/i,J=/^h\d$/i,K=/^[^{]+\{\s*\[native \w/,Z=/^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,ee=/[+~]/,te=new RegExp("\\\\[\\da-fA-F]{1,6}"+M+"?|\\\\([^\\r\\n\\f])","g"),ne=function(e,t){var n="0x"+e.slice(1)-65536;return t||(n<0?String.fromCharCode(n+65536):String.fromCharCode(n>>10|55296,1023&n|56320))},re=/([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,ie=function(e,t){return t?"\0"===e?"\ufffd":e.slice(0,-1)+"\\"+e.charCodeAt(e.length-1).toString(16)+" ":"\\"+e},oe=function(){T()},ae=be(function(e){return!0===e.disabled&&"fieldset"===e.nodeName.toLowerCase()},{dir:"parentNode",next:"legend"});try{H.apply(t=O.call(p.childNodes),p.childNodes),t[p.childNodes.length].nodeType}catch(e){H={apply:t.length?function(e,t){L.apply(e,O.call(t))}:function(e,t){var n=e.length,r=0;while(e[n++]=t[r++]);e.length=n-1}}}function se(t,e,n,r){var i,o,a,s,u,l,c,f=e&&e.ownerDocument,p=e?e.nodeType:9;if(n=n||[],"string"!=typeof t||!t||1!==p&&9!==p&&11!==p)return n;if(!r&&(T(e),e=e||C,E)){if(11!==p&&(u=Z.exec(t)))if(i=u[1]){if(9===p){if(!(a=e.getElementById(i)))return n;if(a.id===i)return n.push(a),n}else if(f&&(a=f.getElementById(i))&&y(e,a)&&a.id===i)return n.push(a),n}else{if(u[2])return H.apply(n,e.getElementsByTagName(t)),n;if((i=u[3])&&d.getElementsByClassName&&e.getElementsByClassName)return H.apply(n,e.getElementsByClassName(i)),n}if(d.qsa&&!N[t+" "]&&(!v||!v.test(t))&&(1!==p||"object"!==e.nodeName.toLowerCase())){if(c=t,f=e,1===p&&(U.test(t)||z.test(t))){(f=ee.test(t)&&ye(e.parentNode)||e)===e&&d.scope||((s=e.getAttribute("id"))?s=s.replace(re,ie):e.setAttribute("id",s=S)),o=(l=h(t)).length;while(o--)l[o]=(s?"#"+s:":scope")+" "+xe(l[o]);c=l.join(",")}try{return H.apply(n,f.querySelectorAll(c)),n}catch(e){N(t,!0)}finally{s===S&&e.removeAttribute("id")}}}return g(t.replace($,"$1"),e,n,r)}function ue(){var r=[];return function e(t,n){return r.push(t+" ")>b.cacheLength&&delete e[r.shift()],e[t+" "]=n}}function le(e){return e[S]=!0,e}function ce(e){var t=C.createElement("fieldset");try{return!!e(t)}catch(e){return!1}finally{t.parentNode&&t.parentNode.removeChild(t),t=null}}function fe(e,t){var n=e.split("|"),r=n.length;while(r--)b.attrHandle[n[r]]=t}function pe(e,t){var n=t&&e,r=n&&1===e.nodeType&&1===t.nodeType&&e.sourceIndex-t.sourceIndex;if(r)return r;if(n)while(n=n.nextSibling)if(n===t)return-1;return e?1:-1}function de(t){return function(e){return"input"===e.nodeName.toLowerCase()&&e.type===t}}function he(n){return function(e){var t=e.nodeName.toLowerCase();return("input"===t||"button"===t)&&e.type===n}}function ge(t){return function(e){return"form"in e?e.parentNode&&!1===e.disabled?"label"in e?"label"in e.parentNode?e.parentNode.disabled===t:e.disabled===t:e.isDisabled===t||e.isDisabled!==!t&&ae(e)===t:e.disabled===t:"label"in e&&e.disabled===t}}function ve(a){return le(function(o){return o=+o,le(function(e,t){var n,r=a([],e.length,o),i=r.length;while(i--)e[n=r[i]]&&(e[n]=!(t[n]=e[n]))})})}function ye(e){return e&&"undefined"!=typeof e.getElementsByTagName&&e}for(e in d=se.support={},i=se.isXML=function(e){var t=e&&e.namespaceURI,n=e&&(e.ownerDocument||e).documentElement;return!Y.test(t||n&&n.nodeName||"HTML")},T=se.setDocument=function(e){var t,n,r=e?e.ownerDocument||e:p;return r!=C&&9===r.nodeType&&r.documentElement&&(a=(C=r).documentElement,E=!i(C),p!=C&&(n=C.defaultView)&&n.top!==n&&(n.addEventListener?n.addEventListener("unload",oe,!1):n.attachEvent&&n.attachEvent("onunload",oe)),d.scope=ce(function(e){return a.appendChild(e).appendChild(C.createElement("div")),"undefined"!=typeof e.querySelectorAll&&!e.querySelectorAll(":scope fieldset div").length}),d.attributes=ce(function(e){return e.className="i",!e.getAttribute("className")}),d.getElementsByTagName=ce(function(e){return e.appendChild(C.createComment("")),!e.getElementsByTagName("*").length}),d.getElementsByClassName=K.test(C.getElementsByClassName),d.getById=ce(function(e){return a.appendChild(e).id=S,!C.getElementsByName||!C.getElementsByName(S).length}),d.getById?(b.filter.ID=function(e){var t=e.replace(te,ne);return function(e){return e.getAttribute("id")===t}},b.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&E){var n=t.getElementById(e);return n?[n]:[]}}):(b.filter.ID=function(e){var n=e.replace(te,ne);return function(e){var t="undefined"!=typeof e.getAttributeNode&&e.getAttributeNode("id");return t&&t.value===n}},b.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&E){var n,r,i,o=t.getElementById(e);if(o){if((n=o.getAttributeNode("id"))&&n.value===e)return[o];i=t.getElementsByName(e),r=0;while(o=i[r++])if((n=o.getAttributeNode("id"))&&n.value===e)return[o]}return[]}}),b.find.TAG=d.getElementsByTagName?function(e,t){return"undefined"!=typeof t.getElementsByTagName?t.getElementsByTagName(e):d.qsa?t.querySelectorAll(e):void 0}:function(e,t){var n,r=[],i=0,o=t.getElementsByTagName(e);if("*"===e){while(n=o[i++])1===n.nodeType&&r.push(n);return r}return o},b.find.CLASS=d.getElementsByClassName&&function(e,t){if("undefined"!=typeof t.getElementsByClassName&&E)return t.getElementsByClassName(e)},s=[],v=[],(d.qsa=K.test(C.querySelectorAll))&&(ce(function(e){var t;a.appendChild(e).innerHTML="<a id='"+S+"'></a><select id='"+S+"-\r\\' msallowcapture=''><option selected=''></option></select>",e.querySelectorAll("[msallowcapture^='']").length&&v.push("[*^$]="+M+"*(?:''|\"\")"),e.querySelectorAll("[selected]").length||v.push("\\["+M+"*(?:value|"+R+")"),e.querySelectorAll("[id~="+S+"-]").length||v.push("~="),(t=C.createElement("input")).setAttribute("name",""),e.appendChild(t),e.querySelectorAll("[name='']").length||v.push("\\["+M+"*name"+M+"*="+M+"*(?:''|\"\")"),e.querySelectorAll(":checked").length||v.push(":checked"),e.querySelectorAll("a#"+S+"+*").length||v.push(".#.+[+~]"),e.querySelectorAll("\\\f"),v.push("[\\r\\n\\f]")}),ce(function(e){e.innerHTML="<a href='' disabled='disabled'></a><select disabled='disabled'><option/></select>";var t=C.createElement("input");t.setAttribute("type","hidden"),e.appendChild(t).setAttribute("name","D"),e.querySelectorAll("[name=d]").length&&v.push("name"+M+"*[*^$|!~]?="),2!==e.querySelectorAll(":enabled").length&&v.push(":enabled",":disabled"),a.appendChild(e).disabled=!0,2!==e.querySelectorAll(":disabled").length&&v.push(":enabled",":disabled"),e.querySelectorAll("*,:x"),v.push(",.*:")})),(d.matchesSelector=K.test(c=a.matches||a.webkitMatchesSelector||a.mozMatchesSelector||a.oMatchesSelector||a.msMatchesSelector))&&ce(function(e){d.disconnectedMatch=c.call(e,"*"),c.call(e,"[s!='']:x"),s.push("!=",F)}),v=v.length&&new RegExp(v.join("|")),s=s.length&&new RegExp(s.join("|")),t=K.test(a.compareDocumentPosition),y=t||K.test(a.contains)?function(e,t){var n=9===e.nodeType?e.documentElement:e,r=t&&t.parentNode;return e===r||!(!r||1!==r.nodeType||!(n.contains?n.contains(r):e.compareDocumentPosition&&16&e.compareDocumentPosition(r)))}:function(e,t){if(t)while(t=t.parentNode)if(t===e)return!0;return!1},j=t?function(e,t){if(e===t)return l=!0,0;var n=!e.compareDocumentPosition-!t.compareDocumentPosition;return n||(1&(n=(e.ownerDocument||e)==(t.ownerDocument||t)?e.compareDocumentPosition(t):1)||!d.sortDetached&&t.compareDocumentPosition(e)===n?e==C||e.ownerDocument==p&&y(p,e)?-1:t==C||t.ownerDocument==p&&y(p,t)?1:u?P(u,e)-P(u,t):0:4&n?-1:1)}:function(e,t){if(e===t)return l=!0,0;var n,r=0,i=e.parentNode,o=t.parentNode,a=[e],s=[t];if(!i||!o)return e==C?-1:t==C?1:i?-1:o?1:u?P(u,e)-P(u,t):0;if(i===o)return pe(e,t);n=e;while(n=n.parentNode)a.unshift(n);n=t;while(n=n.parentNode)s.unshift(n);while(a[r]===s[r])r++;return r?pe(a[r],s[r]):a[r]==p?-1:s[r]==p?1:0}),C},se.matches=function(e,t){return se(e,null,null,t)},se.matchesSelector=function(e,t){if(T(e),d.matchesSelector&&E&&!N[t+" "]&&(!s||!s.test(t))&&(!v||!v.test(t)))try{var n=c.call(e,t);if(n||d.disconnectedMatch||e.document&&11!==e.document.nodeType)return n}catch(e){N(t,!0)}return 0<se(t,C,null,[e]).length},se.contains=function(e,t){return(e.ownerDocument||e)!=C&&T(e),y(e,t)},se.attr=function(e,t){(e.ownerDocument||e)!=C&&T(e);var n=b.attrHandle[t.toLowerCase()],r=n&&D.call(b.attrHandle,t.toLowerCase())?n(e,t,!E):void 0;return void 0!==r?r:d.attributes||!E?e.getAttribute(t):(r=e.getAttributeNode(t))&&r.specified?r.value:null},se.escape=function(e){return(e+"").replace(re,ie)},se.error=function(e){throw new Error("Syntax error, unrecognized expression: "+e)},se.uniqueSort=function(e){var t,n=[],r=0,i=0;if(l=!d.detectDuplicates,u=!d.sortStable&&e.slice(0),e.sort(j),l){while(t=e[i++])t===e[i]&&(r=n.push(i));while(r--)e.splice(n[r],1)}return u=null,e},o=se.getText=function(e){var t,n="",r=0,i=e.nodeType;if(i){if(1===i||9===i||11===i){if("string"==typeof e.textContent)return e.textContent;for(e=e.firstChild;e;e=e.nextSibling)n+=o(e)}else if(3===i||4===i)return e.nodeValue}else while(t=e[r++])n+=o(t);return n},(b=se.selectors={cacheLength:50,createPseudo:le,match:G,attrHandle:{},find:{},relative:{">":{dir:"parentNode",first:!0}," ":{dir:"parentNode"},"+":{dir:"previousSibling",first:!0},"~":{dir:"previousSibling"}},preFilter:{ATTR:function(e){return e[1]=e[1].replace(te,ne),e[3]=(e[3]||e[4]||e[5]||"").replace(te,ne),"~="===e[2]&&(e[3]=" "+e[3]+" "),e.slice(0,4)},CHILD:function(e){return e[1]=e[1].toLowerCase(),"nth"===e[1].slice(0,3)?(e[3]||se.error(e[0]),e[4]=+(e[4]?e[5]+(e[6]||1):2*("even"===e[3]||"odd"===e[3])),e[5]=+(e[7]+e[8]||"odd"===e[3])):e[3]&&se.error(e[0]),e},PSEUDO:function(e){var t,n=!e[6]&&e[2];return G.CHILD.test(e[0])?null:(e[3]?e[2]=e[4]||e[5]||"":n&&X.test(n)&&(t=h(n,!0))&&(t=n.indexOf(")",n.length-t)-n.length)&&(e[0]=e[0].slice(0,t),e[2]=n.slice(0,t)),e.slice(0,3))}},filter:{TAG:function(e){var t=e.replace(te,ne).toLowerCase();return"*"===e?function(){return!0}:function(e){return e.nodeName&&e.nodeName.toLowerCase()===t}},CLASS:function(e){var t=m[e+" "];return t||(t=new RegExp("(^|"+M+")"+e+"("+M+"|$)"))&&m(e,function(e){return t.test("string"==typeof e.className&&e.className||"undefined"!=typeof e.getAttribute&&e.getAttribute("class")||"")})},ATTR:function(n,r,i){return function(e){var t=se.attr(e,n);return null==t?"!="===r:!r||(t+="","="===r?t===i:"!="===r?t!==i:"^="===r?i&&0===t.indexOf(i):"*="===r?i&&-1<t.indexOf(i):"$="===r?i&&t.slice(-i.length)===i:"~="===r?-1<(" "+t.replace(B," ")+" ").indexOf(i):"|="===r&&(t===i||t.slice(0,i.length+1)===i+"-"))}},CHILD:function(h,e,t,g,v){var y="nth"!==h.slice(0,3),m="last"!==h.slice(-4),x="of-type"===e;return 1===g&&0===v?function(e){return!!e.parentNode}:function(e,t,n){var r,i,o,a,s,u,l=y!==m?"nextSibling":"previousSibling",c=e.parentNode,f=x&&e.nodeName.toLowerCase(),p=!n&&!x,d=!1;if(c){if(y){while(l){a=e;while(a=a[l])if(x?a.nodeName.toLowerCase()===f:1===a.nodeType)return!1;u=l="only"===h&&!u&&"nextSibling"}return!0}if(u=[m?c.firstChild:c.lastChild],m&&p){d=(s=(r=(i=(o=(a=c)[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]||[])[0]===k&&r[1])&&r[2],a=s&&c.childNodes[s];while(a=++s&&a&&a[l]||(d=s=0)||u.pop())if(1===a.nodeType&&++d&&a===e){i[h]=[k,s,d];break}}else if(p&&(d=s=(r=(i=(o=(a=e)[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]||[])[0]===k&&r[1]),!1===d)while(a=++s&&a&&a[l]||(d=s=0)||u.pop())if((x?a.nodeName.toLowerCase()===f:1===a.nodeType)&&++d&&(p&&((i=(o=a[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]=[k,d]),a===e))break;return(d-=v)===g||d%g==0&&0<=d/g}}},PSEUDO:function(e,o){var t,a=b.pseudos[e]||b.setFilters[e.toLowerCase()]||se.error("unsupported pseudo: "+e);return a[S]?a(o):1<a.length?(t=[e,e,"",o],b.setFilters.hasOwnProperty(e.toLowerCase())?le(function(e,t){var n,r=a(e,o),i=r.length;while(i--)e[n=P(e,r[i])]=!(t[n]=r[i])}):function(e){return a(e,0,t)}):a}},pseudos:{not:le(function(e){var r=[],i=[],s=f(e.replace($,"$1"));return s[S]?le(function(e,t,n,r){var i,o=s(e,null,r,[]),a=e.length;while(a--)(i=o[a])&&(e[a]=!(t[a]=i))}):function(e,t,n){return r[0]=e,s(r,null,n,i),r[0]=null,!i.pop()}}),has:le(function(t){return function(e){return 0<se(t,e).length}}),contains:le(function(t){return t=t.replace(te,ne),function(e){return-1<(e.textContent||o(e)).indexOf(t)}}),lang:le(function(n){return V.test(n||"")||se.error("unsupported lang: "+n),n=n.replace(te,ne).toLowerCase(),function(e){var t;do{if(t=E?e.lang:e.getAttribute("xml:lang")||e.getAttribute("lang"))return(t=t.toLowerCase())===n||0===t.indexOf(n+"-")}while((e=e.parentNode)&&1===e.nodeType);return!1}}),target:function(e){var t=n.location&&n.location.hash;return t&&t.slice(1)===e.id},root:function(e){return e===a},focus:function(e){return e===C.activeElement&&(!C.hasFocus||C.hasFocus())&&!!(e.type||e.href||~e.tabIndex)},enabled:ge(!1),disabled:ge(!0),checked:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&!!e.checked||"option"===t&&!!e.selected},selected:function(e){return e.parentNode&&e.parentNode.selectedIndex,!0===e.selected},empty:function(e){for(e=e.firstChild;e;e=e.nextSibling)if(e.nodeType<6)return!1;return!0},parent:function(e){return!b.pseudos.empty(e)},header:function(e){return J.test(e.nodeName)},input:function(e){return Q.test(e.nodeName)},button:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&"button"===e.type||"button"===t},text:function(e){var t;return"input"===e.nodeName.toLowerCase()&&"text"===e.type&&(null==(t=e.getAttribute("type"))||"text"===t.toLowerCase())},first:ve(function(){return[0]}),last:ve(function(e,t){return[t-1]}),eq:ve(function(e,t,n){return[n<0?n+t:n]}),even:ve(function(e,t){for(var n=0;n<t;n+=2)e.push(n);return e}),odd:ve(function(e,t){for(var n=1;n<t;n+=2)e.push(n);return e}),lt:ve(function(e,t,n){for(var r=n<0?n+t:t<n?t:n;0<=--r;)e.push(r);return e}),gt:ve(function(e,t,n){for(var r=n<0?n+t:n;++r<t;)e.push(r);return e})}}).pseudos.nth=b.pseudos.eq,{radio:!0,checkbox:!0,file:!0,password:!0,image:!0})b.pseudos[e]=de(e);for(e in{submit:!0,reset:!0})b.pseudos[e]=he(e);function me(){}function xe(e){for(var t=0,n=e.length,r="";t<n;t++)r+=e[t].value;return r}function be(s,e,t){var u=e.dir,l=e.next,c=l||u,f=t&&"parentNode"===c,p=r++;return e.first?function(e,t,n){while(e=e[u])if(1===e.nodeType||f)return s(e,t,n);return!1}:function(e,t,n){var r,i,o,a=[k,p];if(n){while(e=e[u])if((1===e.nodeType||f)&&s(e,t,n))return!0}else while(e=e[u])if(1===e.nodeType||f)if(i=(o=e[S]||(e[S]={}))[e.uniqueID]||(o[e.uniqueID]={}),l&&l===e.nodeName.toLowerCase())e=e[u]||e;else{if((r=i[c])&&r[0]===k&&r[1]===p)return a[2]=r[2];if((i[c]=a)[2]=s(e,t,n))return!0}return!1}}function we(i){return 1<i.length?function(e,t,n){var r=i.length;while(r--)if(!i[r](e,t,n))return!1;return!0}:i[0]}function Te(e,t,n,r,i){for(var o,a=[],s=0,u=e.length,l=null!=t;s<u;s++)(o=e[s])&&(n&&!n(o,r,i)||(a.push(o),l&&t.push(s)));return a}function Ce(d,h,g,v,y,e){return v&&!v[S]&&(v=Ce(v)),y&&!y[S]&&(y=Ce(y,e)),le(function(e,t,n,r){var i,o,a,s=[],u=[],l=t.length,c=e||function(e,t,n){for(var r=0,i=t.length;r<i;r++)se(e,t[r],n);return n}(h||"*",n.nodeType?[n]:n,[]),f=!d||!e&&h?c:Te(c,s,d,n,r),p=g?y||(e?d:l||v)?[]:t:f;if(g&&g(f,p,n,r),v){i=Te(p,u),v(i,[],n,r),o=i.length;while(o--)(a=i[o])&&(p[u[o]]=!(f[u[o]]=a))}if(e){if(y||d){if(y){i=[],o=p.length;while(o--)(a=p[o])&&i.push(f[o]=a);y(null,p=[],i,r)}o=p.length;while(o--)(a=p[o])&&-1<(i=y?P(e,a):s[o])&&(e[i]=!(t[i]=a))}}else p=Te(p===t?p.splice(l,p.length):p),y?y(null,t,p,r):H.apply(t,p)})}function Ee(e){for(var i,t,n,r=e.length,o=b.relative[e[0].type],a=o||b.relative[" "],s=o?1:0,u=be(function(e){return e===i},a,!0),l=be(function(e){return-1<P(i,e)},a,!0),c=[function(e,t,n){var r=!o&&(n||t!==w)||((i=t).nodeType?u(e,t,n):l(e,t,n));return i=null,r}];s<r;s++)if(t=b.relative[e[s].type])c=[be(we(c),t)];else{if((t=b.filter[e[s].type].apply(null,e[s].matches))[S]){for(n=++s;n<r;n++)if(b.relative[e[n].type])break;return Ce(1<s&&we(c),1<s&&xe(e.slice(0,s-1).concat({value:" "===e[s-2].type?"*":""})).replace($,"$1"),t,s<n&&Ee(e.slice(s,n)),n<r&&Ee(e=e.slice(n)),n<r&&xe(e))}c.push(t)}return we(c)}return me.prototype=b.filters=b.pseudos,b.setFilters=new me,h=se.tokenize=function(e,t){var n,r,i,o,a,s,u,l=x[e+" "];if(l)return t?0:l.slice(0);a=e,s=[],u=b.preFilter;while(a){for(o in n&&!(r=_.exec(a))||(r&&(a=a.slice(r[0].length)||a),s.push(i=[])),n=!1,(r=z.exec(a))&&(n=r.shift(),i.push({value:n,type:r[0].replace($," ")}),a=a.slice(n.length)),b.filter)!(r=G[o].exec(a))||u[o]&&!(r=u[o](r))||(n=r.shift(),i.push({value:n,type:o,matches:r}),a=a.slice(n.length));if(!n)break}return t?a.length:a?se.error(e):x(e,s).slice(0)},f=se.compile=function(e,t){var n,v,y,m,x,r,i=[],o=[],a=A[e+" "];if(!a){t||(t=h(e)),n=t.length;while(n--)(a=Ee(t[n]))[S]?i.push(a):o.push(a);(a=A(e,(v=o,m=0<(y=i).length,x=0<v.length,r=function(e,t,n,r,i){var o,a,s,u=0,l="0",c=e&&[],f=[],p=w,d=e||x&&b.find.TAG("*",i),h=k+=null==p?1:Math.random()||.1,g=d.length;for(i&&(w=t==C||t||i);l!==g&&null!=(o=d[l]);l++){if(x&&o){a=0,t||o.ownerDocument==C||(T(o),n=!E);while(s=v[a++])if(s(o,t||C,n)){r.push(o);break}i&&(k=h)}m&&((o=!s&&o)&&u--,e&&c.push(o))}if(u+=l,m&&l!==u){a=0;while(s=y[a++])s(c,f,t,n);if(e){if(0<u)while(l--)c[l]||f[l]||(f[l]=q.call(r));f=Te(f)}H.apply(r,f),i&&!e&&0<f.length&&1<u+y.length&&se.uniqueSort(r)}return i&&(k=h,w=p),c},m?le(r):r))).selector=e}return a},g=se.select=function(e,t,n,r){var i,o,a,s,u,l="function"==typeof e&&e,c=!r&&h(e=l.selector||e);if(n=n||[],1===c.length){if(2<(o=c[0]=c[0].slice(0)).length&&"ID"===(a=o[0]).type&&9===t.nodeType&&E&&b.relative[o[1].type]){if(!(t=(b.find.ID(a.matches[0].replace(te,ne),t)||[])[0]))return n;l&&(t=t.parentNode),e=e.slice(o.shift().value.length)}i=G.needsContext.test(e)?0:o.length;while(i--){if(a=o[i],b.relative[s=a.type])break;if((u=b.find[s])&&(r=u(a.matches[0].replace(te,ne),ee.test(o[0].type)&&ye(t.parentNode)||t))){if(o.splice(i,1),!(e=r.length&&xe(o)))return H.apply(n,r),n;break}}}return(l||f(e,c))(r,t,!E,n,!t||ee.test(e)&&ye(t.parentNode)||t),n},d.sortStable=S.split("").sort(j).join("")===S,d.detectDuplicates=!!l,T(),d.sortDetached=ce(function(e){return 1&e.compareDocumentPosition(C.createElement("fieldset"))}),ce(function(e){return e.innerHTML="<a href='#'></a>","#"===e.firstChild.getAttribute("href")})||fe("type|href|height|width",function(e,t,n){if(!n)return e.getAttribute(t,"type"===t.toLowerCase()?1:2)}),d.attributes&&ce(function(e){return e.innerHTML="<input/>",e.firstChild.setAttribute("value",""),""===e.firstChild.getAttribute("value")})||fe("value",function(e,t,n){if(!n&&"input"===e.nodeName.toLowerCase())return e.defaultValue}),ce(function(e){return null==e.getAttribute("disabled")})||fe(R,function(e,t,n){var r;if(!n)return!0===e[t]?t.toLowerCase():(r=e.getAttributeNode(t))&&r.specified?r.value:null}),se}(C);S.find=d,S.expr=d.selectors,S.expr[":"]=S.expr.pseudos,S.uniqueSort=S.unique=d.uniqueSort,S.text=d.getText,S.isXMLDoc=d.isXML,S.contains=d.contains,S.escapeSelector=d.escape;var h=function(e,t,n){var r=[],i=void 0!==n;while((e=e[t])&&9!==e.nodeType)if(1===e.nodeType){if(i&&S(e).is(n))break;r.push(e)}return r},T=function(e,t){for(var n=[];e;e=e.nextSibling)1===e.nodeType&&e!==t&&n.push(e);return n},k=S.expr.match.needsContext;function A(e,t){return e.nodeName&&e.nodeName.toLowerCase()===t.toLowerCase()}var N=/^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i;function j(e,n,r){return m(n)?S.grep(e,function(e,t){return!!n.call(e,t,e)!==r}):n.nodeType?S.grep(e,function(e){return e===n!==r}):"string"!=typeof n?S.grep(e,function(e){return-1<i.call(n,e)!==r}):S.filter(n,e,r)}S.filter=function(e,t,n){var r=t[0];return n&&(e=":not("+e+")"),1===t.length&&1===r.nodeType?S.find.matchesSelector(r,e)?[r]:[]:S.find.matches(e,S.grep(t,function(e){return 1===e.nodeType}))},S.fn.extend({find:function(e){var t,n,r=this.length,i=this;if("string"!=typeof e)return this.pushStack(S(e).filter(function(){for(t=0;t<r;t++)if(S.contains(i[t],this))return!0}));for(n=this.pushStack([]),t=0;t<r;t++)S.find(e,i[t],n);return 1<r?S.uniqueSort(n):n},filter:function(e){return this.pushStack(j(this,e||[],!1))},not:function(e){return this.pushStack(j(this,e||[],!0))},is:function(e){return!!j(this,"string"==typeof e&&k.test(e)?S(e):e||[],!1).length}});var D,q=/^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/;(S.fn.init=function(e,t,n){var r,i;if(!e)return this;if(n=n||D,"string"==typeof e){if(!(r="<"===e[0]&&">"===e[e.length-1]&&3<=e.length?[null,e,null]:q.exec(e))||!r[1]&&t)return!t||t.jquery?(t||n).find(e):this.constructor(t).find(e);if(r[1]){if(t=t instanceof S?t[0]:t,S.merge(this,S.parseHTML(r[1],t&&t.nodeType?t.ownerDocument||t:E,!0)),N.test(r[1])&&S.isPlainObject(t))for(r in t)m(this[r])?this[r](t[r]):this.attr(r,t[r]);return this}return(i=E.getElementById(r[2]))&&(this[0]=i,this.length=1),this}return e.nodeType?(this[0]=e,this.length=1,this):m(e)?void 0!==n.ready?n.ready(e):e(S):S.makeArray(e,this)}).prototype=S.fn,D=S(E);var L=/^(?:parents|prev(?:Until|All))/,H={children:!0,contents:!0,next:!0,prev:!0};function O(e,t){while((e=e[t])&&1!==e.nodeType);return e}S.fn.extend({has:function(e){var t=S(e,this),n=t.length;return this.filter(function(){for(var e=0;e<n;e++)if(S.contains(this,t[e]))return!0})},closest:function(e,t){var n,r=0,i=this.length,o=[],a="string"!=typeof e&&S(e);if(!k.test(e))for(;r<i;r++)for(n=this[r];n&&n!==t;n=n.parentNode)if(n.nodeType<11&&(a?-1<a.index(n):1===n.nodeType&&S.find.matchesSelector(n,e))){o.push(n);break}return this.pushStack(1<o.length?S.uniqueSort(o):o)},index:function(e){return e?"string"==typeof e?i.call(S(e),this[0]):i.call(this,e.jquery?e[0]:e):this[0]&&this[0].parentNode?this.first().prevAll().length:-1},add:function(e,t){return this.pushStack(S.uniqueSort(S.merge(this.get(),S(e,t))))},addBack:function(e){return this.add(null==e?this.prevObject:this.prevObject.filter(e))}}),S.each({parent:function(e){var t=e.parentNode;return t&&11!==t.nodeType?t:null},parents:function(e){return h(e,"parentNode")},parentsUntil:function(e,t,n){return h(e,"parentNode",n)},next:function(e){return O(e,"nextSibling")},prev:function(e){return O(e,"previousSibling")},nextAll:function(e){return h(e,"nextSibling")},prevAll:function(e){return h(e,"previousSibling")},nextUntil:function(e,t,n){return h(e,"nextSibling",n)},prevUntil:function(e,t,n){return h(e,"previousSibling",n)},siblings:function(e){return T((e.parentNode||{}).firstChild,e)},children:function(e){return T(e.firstChild)},contents:function(e){return null!=e.contentDocument&&r(e.contentDocument)?e.contentDocument:(A(e,"template")&&(e=e.content||e),S.merge([],e.childNodes))}},function(r,i){S.fn[r]=function(e,t){var n=S.map(this,i,e);return"Until"!==r.slice(-5)&&(t=e),t&&"string"==typeof t&&(n=S.filter(t,n)),1<this.length&&(H[r]||S.uniqueSort(n),L.test(r)&&n.reverse()),this.pushStack(n)}});var P=/[^\x20\t\r\n\f]+/g;function R(e){return e}function M(e){throw e}function I(e,t,n,r){var i;try{e&&m(i=e.promise)?i.call(e).done(t).fail(n):e&&m(i=e.then)?i.call(e,t,n):t.apply(void 0,[e].slice(r))}catch(e){n.apply(void 0,[e])}}S.Callbacks=function(r){var e,n;r="string"==typeof r?(e=r,n={},S.each(e.match(P)||[],function(e,t){n[t]=!0}),n):S.extend({},r);var i,t,o,a,s=[],u=[],l=-1,c=function(){for(a=a||r.once,o=i=!0;u.length;l=-1){t=u.shift();while(++l<s.length)!1===s[l].apply(t[0],t[1])&&r.stopOnFalse&&(l=s.length,t=!1)}r.memory||(t=!1),i=!1,a&&(s=t?[]:"")},f={add:function(){return s&&(t&&!i&&(l=s.length-1,u.push(t)),function n(e){S.each(e,function(e,t){m(t)?r.unique&&f.has(t)||s.push(t):t&&t.length&&"string"!==w(t)&&n(t)})}(arguments),t&&!i&&c()),this},remove:function(){return S.each(arguments,function(e,t){var n;while(-1<(n=S.inArray(t,s,n)))s.splice(n,1),n<=l&&l--}),this},has:function(e){return e?-1<S.inArray(e,s):0<s.length},empty:function(){return s&&(s=[]),this},disable:function(){return a=u=[],s=t="",this},disabled:function(){return!s},lock:function(){return a=u=[],t||i||(s=t=""),this},locked:function(){return!!a},fireWith:function(e,t){return a||(t=[e,(t=t||[]).slice?t.slice():t],u.push(t),i||c()),this},fire:function(){return f.fireWith(this,arguments),this},fired:function(){return!!o}};return f},S.extend({Deferred:function(e){var o=[["notify","progress",S.Callbacks("memory"),S.Callbacks("memory"),2],["resolve","done",S.Callbacks("once memory"),S.Callbacks("once memory"),0,"resolved"],["reject","fail",S.Callbacks("once memory"),S.Callbacks("once memory"),1,"rejected"]],i="pending",a={state:function(){return i},always:function(){return s.done(arguments).fail(arguments),this},"catch":function(e){return a.then(null,e)},pipe:function(){var i=arguments;return S.Deferred(function(r){S.each(o,function(e,t){var n=m(i[t[4]])&&i[t[4]];s[t[1]](function(){var e=n&&n.apply(this,arguments);e&&m(e.promise)?e.promise().progress(r.notify).done(r.resolve).fail(r.reject):r[t[0]+"With"](this,n?[e]:arguments)})}),i=null}).promise()},then:function(t,n,r){var u=0;function l(i,o,a,s){return function(){var n=this,r=arguments,e=function(){var e,t;if(!(i<u)){if((e=a.apply(n,r))===o.promise())throw new TypeError("Thenable self-resolution");t=e&&("object"==typeof e||"function"==typeof e)&&e.then,m(t)?s?t.call(e,l(u,o,R,s),l(u,o,M,s)):(u++,t.call(e,l(u,o,R,s),l(u,o,M,s),l(u,o,R,o.notifyWith))):(a!==R&&(n=void 0,r=[e]),(s||o.resolveWith)(n,r))}},t=s?e:function(){try{e()}catch(e){S.Deferred.exceptionHook&&S.Deferred.exceptionHook(e,t.stackTrace),u<=i+1&&(a!==M&&(n=void 0,r=[e]),o.rejectWith(n,r))}};i?t():(S.Deferred.getStackHook&&(t.stackTrace=S.Deferred.getStackHook()),C.setTimeout(t))}}return S.Deferred(function(e){o[0][3].add(l(0,e,m(r)?r:R,e.notifyWith)),o[1][3].add(l(0,e,m(t)?t:R)),o[2][3].add(l(0,e,m(n)?n:M))}).promise()},promise:function(e){return null!=e?S.extend(e,a):a}},s={};return S.each(o,function(e,t){var n=t[2],r=t[5];a[t[1]]=n.add,r&&n.add(function(){i=r},o[3-e][2].disable,o[3-e][3].disable,o[0][2].lock,o[0][3].lock),n.add(t[3].fire),s[t[0]]=function(){return s[t[0]+"With"](this===s?void 0:this,arguments),this},s[t[0]+"With"]=n.fireWith}),a.promise(s),e&&e.call(s,s),s},when:function(e){var n=arguments.length,t=n,r=Array(t),i=s.call(arguments),o=S.Deferred(),a=function(t){return function(e){r[t]=this,i[t]=1<arguments.length?s.call(arguments):e,--n||o.resolveWith(r,i)}};if(n<=1&&(I(e,o.done(a(t)).resolve,o.reject,!n),"pending"===o.state()||m(i[t]&&i[t].then)))return o.then();while(t--)I(i[t],a(t),o.reject);return o.promise()}});var W=/^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;S.Deferred.exceptionHook=function(e,t){C.console&&C.console.warn&&e&&W.test(e.name)&&C.console.warn("jQuery.Deferred exception: "+e.message,e.stack,t)},S.readyException=function(e){C.setTimeout(function(){throw e})};var F=S.Deferred();function B(){E.removeEventListener("DOMContentLoaded",B),C.removeEventListener("load",B),S.ready()}S.fn.ready=function(e){return F.then(e)["catch"](function(e){S.readyException(e)}),this},S.extend({isReady:!1,readyWait:1,ready:function(e){(!0===e?--S.readyWait:S.isReady)||(S.isReady=!0)!==e&&0<--S.readyWait||F.resolveWith(E,[S])}}),S.ready.then=F.then,"complete"===E.readyState||"loading"!==E.readyState&&!E.documentElement.doScroll?C.setTimeout(S.ready):(E.addEventListener("DOMContentLoaded",B),C.addEventListener("load",B));var $=function(e,t,n,r,i,o,a){var s=0,u=e.length,l=null==n;if("object"===w(n))for(s in i=!0,n)$(e,t,s,n[s],!0,o,a);else if(void 0!==r&&(i=!0,m(r)||(a=!0),l&&(a?(t.call(e,r),t=null):(l=t,t=function(e,t,n){return l.call(S(e),n)})),t))for(;s<u;s++)t(e[s],n,a?r:r.call(e[s],s,t(e[s],n)));return i?e:l?t.call(e):u?t(e[0],n):o},_=/^-ms-/,z=/-([a-z])/g;function U(e,t){return t.toUpperCase()}function X(e){return e.replace(_,"ms-").replace(z,U)}var V=function(e){return 1===e.nodeType||9===e.nodeType||!+e.nodeType};function G(){this.expando=S.expando+G.uid++}G.uid=1,G.prototype={cache:function(e){var t=e[this.expando];return t||(t={},V(e)&&(e.nodeType?e[this.expando]=t:Object.defineProperty(e,this.expando,{value:t,configurable:!0}))),t},set:function(e,t,n){var r,i=this.cache(e);if("string"==typeof t)i[X(t)]=n;else for(r in t)i[X(r)]=t[r];return i},get:function(e,t){return void 0===t?this.cache(e):e[this.expando]&&e[this.expando][X(t)]},access:function(e,t,n){return void 0===t||t&&"string"==typeof t&&void 0===n?this.get(e,t):(this.set(e,t,n),void 0!==n?n:t)},remove:function(e,t){var n,r=e[this.expando];if(void 0!==r){if(void 0!==t){n=(t=Array.isArray(t)?t.map(X):(t=X(t))in r?[t]:t.match(P)||[]).length;while(n--)delete r[t[n]]}(void 0===t||S.isEmptyObject(r))&&(e.nodeType?e[this.expando]=void 0:delete e[this.expando])}},hasData:function(e){var t=e[this.expando];return void 0!==t&&!S.isEmptyObject(t)}};var Y=new G,Q=new G,J=/^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,K=/[A-Z]/g;function Z(e,t,n){var r,i;if(void 0===n&&1===e.nodeType)if(r="data-"+t.replace(K,"-$&").toLowerCase(),"string"==typeof(n=e.getAttribute(r))){try{n="true"===(i=n)||"false"!==i&&("null"===i?null:i===+i+""?+i:J.test(i)?JSON.parse(i):i)}catch(e){}Q.set(e,t,n)}else n=void 0;return n}S.extend({hasData:function(e){return Q.hasData(e)||Y.hasData(e)},data:function(e,t,n){return Q.access(e,t,n)},removeData:function(e,t){Q.remove(e,t)},_data:function(e,t,n){return Y.access(e,t,n)},_removeData:function(e,t){Y.remove(e,t)}}),S.fn.extend({data:function(n,e){var t,r,i,o=this[0],a=o&&o.attributes;if(void 0===n){if(this.length&&(i=Q.get(o),1===o.nodeType&&!Y.get(o,"hasDataAttrs"))){t=a.length;while(t--)a[t]&&0===(r=a[t].name).indexOf("data-")&&(r=X(r.slice(5)),Z(o,r,i[r]));Y.set(o,"hasDataAttrs",!0)}return i}return"object"==typeof n?this.each(function(){Q.set(this,n)}):$(this,function(e){var t;if(o&&void 0===e)return void 0!==(t=Q.get(o,n))?t:void 0!==(t=Z(o,n))?t:void 0;this.each(function(){Q.set(this,n,e)})},null,e,1<arguments.length,null,!0)},removeData:function(e){return this.each(function(){Q.remove(this,e)})}}),S.extend({queue:function(e,t,n){var r;if(e)return t=(t||"fx")+"queue",r=Y.get(e,t),n&&(!r||Array.isArray(n)?r=Y.access(e,t,S.makeArray(n)):r.push(n)),r||[]},dequeue:function(e,t){t=t||"fx";var n=S.queue(e,t),r=n.length,i=n.shift(),o=S._queueHooks(e,t);"inprogress"===i&&(i=n.shift(),r--),i&&("fx"===t&&n.unshift("inprogress"),delete o.stop,i.call(e,function(){S.dequeue(e,t)},o)),!r&&o&&o.empty.fire()},_queueHooks:function(e,t){var n=t+"queueHooks";return Y.get(e,n)||Y.access(e,n,{empty:S.Callbacks("once memory").add(function(){Y.remove(e,[t+"queue",n])})})}}),S.fn.extend({queue:function(t,n){var e=2;return"string"!=typeof t&&(n=t,t="fx",e--),arguments.length<e?S.queue(this[0],t):void 0===n?this:this.each(function(){var e=S.queue(this,t,n);S._queueHooks(this,t),"fx"===t&&"inprogress"!==e[0]&&S.dequeue(this,t)})},dequeue:function(e){return this.each(function(){S.dequeue(this,e)})},clearQueue:function(e){return this.queue(e||"fx",[])},promise:function(e,t){var n,r=1,i=S.Deferred(),o=this,a=this.length,s=function(){--r||i.resolveWith(o,[o])};"string"!=typeof e&&(t=e,e=void 0),e=e||"fx";while(a--)(n=Y.get(o[a],e+"queueHooks"))&&n.empty&&(r++,n.empty.add(s));return s(),i.promise(t)}});var ee=/[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/.source,te=new RegExp("^(?:([+-])=|)("+ee+")([a-z%]*)$","i"),ne=["Top","Right","Bottom","Left"],re=E.documentElement,ie=function(e){return S.contains(e.ownerDocument,e)},oe={composed:!0};re.getRootNode&&(ie=function(e){return S.contains(e.ownerDocument,e)||e.getRootNode(oe)===e.ownerDocument});var ae=function(e,t){return"none"===(e=t||e).style.display||""===e.style.display&&ie(e)&&"none"===S.css(e,"display")};function se(e,t,n,r){var i,o,a=20,s=r?function(){return r.cur()}:function(){return S.css(e,t,"")},u=s(),l=n&&n[3]||(S.cssNumber[t]?"":"px"),c=e.nodeType&&(S.cssNumber[t]||"px"!==l&&+u)&&te.exec(S.css(e,t));if(c&&c[3]!==l){u/=2,l=l||c[3],c=+u||1;while(a--)S.style(e,t,c+l),(1-o)*(1-(o=s()/u||.5))<=0&&(a=0),c/=o;c*=2,S.style(e,t,c+l),n=n||[]}return n&&(c=+c||+u||0,i=n[1]?c+(n[1]+1)*n[2]:+n[2],r&&(r.unit=l,r.start=c,r.end=i)),i}var ue={};function le(e,t){for(var n,r,i,o,a,s,u,l=[],c=0,f=e.length;c<f;c++)(r=e[c]).style&&(n=r.style.display,t?("none"===n&&(l[c]=Y.get(r,"display")||null,l[c]||(r.style.display="")),""===r.style.display&&ae(r)&&(l[c]=(u=a=o=void 0,a=(i=r).ownerDocument,s=i.nodeName,(u=ue[s])||(o=a.body.appendChild(a.createElement(s)),u=S.css(o,"display"),o.parentNode.removeChild(o),"none"===u&&(u="block"),ue[s]=u)))):"none"!==n&&(l[c]="none",Y.set(r,"display",n)));for(c=0;c<f;c++)null!=l[c]&&(e[c].style.display=l[c]);return e}S.fn.extend({show:function(){return le(this,!0)},hide:function(){return le(this)},toggle:function(e){return"boolean"==typeof e?e?this.show():this.hide():this.each(function(){ae(this)?S(this).show():S(this).hide()})}});var ce,fe,pe=/^(?:checkbox|radio)$/i,de=/<([a-z][^\/\0>\x20\t\r\n\f]*)/i,he=/^$|^module$|\/(?:java|ecma)script/i;ce=E.createDocumentFragment().appendChild(E.createElement("div")),(fe=E.createElement("input")).setAttribute("type","radio"),fe.setAttribute("checked","checked"),fe.setAttribute("name","t"),ce.appendChild(fe),y.checkClone=ce.cloneNode(!0).cloneNode(!0).lastChild.checked,ce.innerHTML="<textarea>x</textarea>",y.noCloneChecked=!!ce.cloneNode(!0).lastChild.defaultValue,ce.innerHTML="<option></option>",y.option=!!ce.lastChild;var ge={thead:[1,"<table>","</table>"],col:[2,"<table><colgroup>","</colgroup></table>"],tr:[2,"<table><tbody>","</tbody></table>"],td:[3,"<table><tbody><tr>","</tr></tbody></table>"],_default:[0,"",""]};function ve(e,t){var n;return n="undefined"!=typeof e.getElementsByTagName?e.getElementsByTagName(t||"*"):"undefined"!=typeof e.querySelectorAll?e.querySelectorAll(t||"*"):[],void 0===t||t&&A(e,t)?S.merge([e],n):n}function ye(e,t){for(var n=0,r=e.length;n<r;n++)Y.set(e[n],"globalEval",!t||Y.get(t[n],"globalEval"))}ge.tbody=ge.tfoot=ge.colgroup=ge.caption=ge.thead,ge.th=ge.td,y.option||(ge.optgroup=ge.option=[1,"<select multiple='multiple'>","</select>"]);var me=/<|&#?\w+;/;function xe(e,t,n,r,i){for(var o,a,s,u,l,c,f=t.createDocumentFragment(),p=[],d=0,h=e.length;d<h;d++)if((o=e[d])||0===o)if("object"===w(o))S.merge(p,o.nodeType?[o]:o);else if(me.test(o)){a=a||f.appendChild(t.createElement("div")),s=(de.exec(o)||["",""])[1].toLowerCase(),u=ge[s]||ge._default,a.innerHTML=u[1]+S.htmlPrefilter(o)+u[2],c=u[0];while(c--)a=a.lastChild;S.merge(p,a.childNodes),(a=f.firstChild).textContent=""}else p.push(t.createTextNode(o));f.textContent="",d=0;while(o=p[d++])if(r&&-1<S.inArray(o,r))i&&i.push(o);else if(l=ie(o),a=ve(f.appendChild(o),"script"),l&&ye(a),n){c=0;while(o=a[c++])he.test(o.type||"")&&n.push(o)}return f}var be=/^([^.]*)(?:\.(.+)|)/;function we(){return!0}function Te(){return!1}function Ce(e,t){return e===function(){try{return E.activeElement}catch(e){}}()==("focus"===t)}function Ee(e,t,n,r,i,o){var a,s;if("object"==typeof t){for(s in"string"!=typeof n&&(r=r||n,n=void 0),t)Ee(e,s,n,r,t[s],o);return e}if(null==r&&null==i?(i=n,r=n=void 0):null==i&&("string"==typeof n?(i=r,r=void 0):(i=r,r=n,n=void 0)),!1===i)i=Te;else if(!i)return e;return 1===o&&(a=i,(i=function(e){return S().off(e),a.apply(this,arguments)}).guid=a.guid||(a.guid=S.guid++)),e.each(function(){S.event.add(this,t,i,r,n)})}function Se(e,i,o){o?(Y.set(e,i,!1),S.event.add(e,i,{namespace:!1,handler:function(e){var t,n,r=Y.get(this,i);if(1&e.isTrigger&&this[i]){if(r.length)(S.event.special[i]||{}).delegateType&&e.stopPropagation();else if(r=s.call(arguments),Y.set(this,i,r),t=o(this,i),this[i](),r!==(n=Y.get(this,i))||t?Y.set(this,i,!1):n={},r!==n)return e.stopImmediatePropagation(),e.preventDefault(),n&&n.value}else r.length&&(Y.set(this,i,{value:S.event.trigger(S.extend(r[0],S.Event.prototype),r.slice(1),this)}),e.stopImmediatePropagation())}})):void 0===Y.get(e,i)&&S.event.add(e,i,we)}S.event={global:{},add:function(t,e,n,r,i){var o,a,s,u,l,c,f,p,d,h,g,v=Y.get(t);if(V(t)){n.handler&&(n=(o=n).handler,i=o.selector),i&&S.find.matchesSelector(re,i),n.guid||(n.guid=S.guid++),(u=v.events)||(u=v.events=Object.create(null)),(a=v.handle)||(a=v.handle=function(e){return"undefined"!=typeof S&&S.event.triggered!==e.type?S.event.dispatch.apply(t,arguments):void 0}),l=(e=(e||"").match(P)||[""]).length;while(l--)d=g=(s=be.exec(e[l])||[])[1],h=(s[2]||"").split(".").sort(),d&&(f=S.event.special[d]||{},d=(i?f.delegateType:f.bindType)||d,f=S.event.special[d]||{},c=S.extend({type:d,origType:g,data:r,handler:n,guid:n.guid,selector:i,needsContext:i&&S.expr.match.needsContext.test(i),namespace:h.join(".")},o),(p=u[d])||((p=u[d]=[]).delegateCount=0,f.setup&&!1!==f.setup.call(t,r,h,a)||t.addEventListener&&t.addEventListener(d,a)),f.add&&(f.add.call(t,c),c.handler.guid||(c.handler.guid=n.guid)),i?p.splice(p.delegateCount++,0,c):p.push(c),S.event.global[d]=!0)}},remove:function(e,t,n,r,i){var o,a,s,u,l,c,f,p,d,h,g,v=Y.hasData(e)&&Y.get(e);if(v&&(u=v.events)){l=(t=(t||"").match(P)||[""]).length;while(l--)if(d=g=(s=be.exec(t[l])||[])[1],h=(s[2]||"").split(".").sort(),d){f=S.event.special[d]||{},p=u[d=(r?f.delegateType:f.bindType)||d]||[],s=s[2]&&new RegExp("(^|\\.)"+h.join("\\.(?:.*\\.|)")+"(\\.|$)"),a=o=p.length;while(o--)c=p[o],!i&&g!==c.origType||n&&n.guid!==c.guid||s&&!s.test(c.namespace)||r&&r!==c.selector&&("**"!==r||!c.selector)||(p.splice(o,1),c.selector&&p.delegateCount--,f.remove&&f.remove.call(e,c));a&&!p.length&&(f.teardown&&!1!==f.teardown.call(e,h,v.handle)||S.removeEvent(e,d,v.handle),delete u[d])}else for(d in u)S.event.remove(e,d+t[l],n,r,!0);S.isEmptyObject(u)&&Y.remove(e,"handle events")}},dispatch:function(e){var t,n,r,i,o,a,s=new Array(arguments.length),u=S.event.fix(e),l=(Y.get(this,"events")||Object.create(null))[u.type]||[],c=S.event.special[u.type]||{};for(s[0]=u,t=1;t<arguments.length;t++)s[t]=arguments[t];if(u.delegateTarget=this,!c.preDispatch||!1!==c.preDispatch.call(this,u)){a=S.event.handlers.call(this,u,l),t=0;while((i=a[t++])&&!u.isPropagationStopped()){u.currentTarget=i.elem,n=0;while((o=i.handlers[n++])&&!u.isImmediatePropagationStopped())u.rnamespace&&!1!==o.namespace&&!u.rnamespace.test(o.namespace)||(u.handleObj=o,u.data=o.data,void 0!==(r=((S.event.special[o.origType]||{}).handle||o.handler).apply(i.elem,s))&&!1===(u.result=r)&&(u.preventDefault(),u.stopPropagation()))}return c.postDispatch&&c.postDispatch.call(this,u),u.result}},handlers:function(e,t){var n,r,i,o,a,s=[],u=t.delegateCount,l=e.target;if(u&&l.nodeType&&!("click"===e.type&&1<=e.button))for(;l!==this;l=l.parentNode||this)if(1===l.nodeType&&("click"!==e.type||!0!==l.disabled)){for(o=[],a={},n=0;n<u;n++)void 0===a[i=(r=t[n]).selector+" "]&&(a[i]=r.needsContext?-1<S(i,this).index(l):S.find(i,this,null,[l]).length),a[i]&&o.push(r);o.length&&s.push({elem:l,handlers:o})}return l=this,u<t.length&&s.push({elem:l,handlers:t.slice(u)}),s},addProp:function(t,e){Object.defineProperty(S.Event.prototype,t,{enumerable:!0,configurable:!0,get:m(e)?function(){if(this.originalEvent)return e(this.originalEvent)}:function(){if(this.originalEvent)return this.originalEvent[t]},set:function(e){Object.defineProperty(this,t,{enumerable:!0,configurable:!0,writable:!0,value:e})}})},fix:function(e){return e[S.expando]?e:new S.Event(e)},special:{load:{noBubble:!0},click:{setup:function(e){var t=this||e;return pe.test(t.type)&&t.click&&A(t,"input")&&Se(t,"click",we),!1},trigger:function(e){var t=this||e;return pe.test(t.type)&&t.click&&A(t,"input")&&Se(t,"click"),!0},_default:function(e){var t=e.target;return pe.test(t.type)&&t.click&&A(t,"input")&&Y.get(t,"click")||A(t,"a")}},beforeunload:{postDispatch:function(e){void 0!==e.result&&e.originalEvent&&(e.originalEvent.returnValue=e.result)}}}},S.removeEvent=function(e,t,n){e.removeEventListener&&e.removeEventListener(t,n)},S.Event=function(e,t){if(!(this instanceof S.Event))return new S.Event(e,t);e&&e.type?(this.originalEvent=e,this.type=e.type,this.isDefaultPrevented=e.defaultPrevented||void 0===e.defaultPrevented&&!1===e.returnValue?we:Te,this.target=e.target&&3===e.target.nodeType?e.target.parentNode:e.target,this.currentTarget=e.currentTarget,this.relatedTarget=e.relatedTarget):this.type=e,t&&S.extend(this,t),this.timeStamp=e&&e.timeStamp||Date.now(),this[S.expando]=!0},S.Event.prototype={constructor:S.Event,isDefaultPrevented:Te,isPropagationStopped:Te,isImmediatePropagationStopped:Te,isSimulated:!1,preventDefault:function(){var e=this.originalEvent;this.isDefaultPrevented=we,e&&!this.isSimulated&&e.preventDefault()},stopPropagation:function(){var e=this.originalEvent;this.isPropagationStopped=we,e&&!this.isSimulated&&e.stopPropagation()},stopImmediatePropagation:function(){var e=this.originalEvent;this.isImmediatePropagationStopped=we,e&&!this.isSimulated&&e.stopImmediatePropagation(),this.stopPropagation()}},S.each({altKey:!0,bubbles:!0,cancelable:!0,changedTouches:!0,ctrlKey:!0,detail:!0,eventPhase:!0,metaKey:!0,pageX:!0,pageY:!0,shiftKey:!0,view:!0,"char":!0,code:!0,charCode:!0,key:!0,keyCode:!0,button:!0,buttons:!0,clientX:!0,clientY:!0,offsetX:!0,offsetY:!0,pointerId:!0,pointerType:!0,screenX:!0,screenY:!0,targetTouches:!0,toElement:!0,touches:!0,which:!0},S.event.addProp),S.each({focus:"focusin",blur:"focusout"},function(e,t){S.event.special[e]={setup:function(){return Se(this,e,Ce),!1},trigger:function(){return Se(this,e),!0},_default:function(){return!0},delegateType:t}}),S.each({mouseenter:"mouseover",mouseleave:"mouseout",pointerenter:"pointerover",pointerleave:"pointerout"},function(e,i){S.event.special[e]={delegateType:i,bindType:i,handle:function(e){var t,n=e.relatedTarget,r=e.handleObj;return n&&(n===this||S.contains(this,n))||(e.type=r.origType,t=r.handler.apply(this,arguments),e.type=i),t}}}),S.fn.extend({on:function(e,t,n,r){return Ee(this,e,t,n,r)},one:function(e,t,n,r){return Ee(this,e,t,n,r,1)},off:function(e,t,n){var r,i;if(e&&e.preventDefault&&e.handleObj)return r=e.handleObj,S(e.delegateTarget).off(r.namespace?r.origType+"."+r.namespace:r.origType,r.selector,r.handler),this;if("object"==typeof e){for(i in e)this.off(i,t,e[i]);return this}return!1!==t&&"function"!=typeof t||(n=t,t=void 0),!1===n&&(n=Te),this.each(function(){S.event.remove(this,e,n,t)})}});var ke=/<script|<style|<link/i,Ae=/checked\s*(?:[^=]|=\s*.checked.)/i,Ne=/^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;function je(e,t){return A(e,"table")&&A(11!==t.nodeType?t:t.firstChild,"tr")&&S(e).children("tbody")[0]||e}function De(e){return e.type=(null!==e.getAttribute("type"))+"/"+e.type,e}function qe(e){return"true/"===(e.type||"").slice(0,5)?e.type=e.type.slice(5):e.removeAttribute("type"),e}function Le(e,t){var n,r,i,o,a,s;if(1===t.nodeType){if(Y.hasData(e)&&(s=Y.get(e).events))for(i in Y.remove(t,"handle events"),s)for(n=0,r=s[i].length;n<r;n++)S.event.add(t,i,s[i][n]);Q.hasData(e)&&(o=Q.access(e),a=S.extend({},o),Q.set(t,a))}}function He(n,r,i,o){r=g(r);var e,t,a,s,u,l,c=0,f=n.length,p=f-1,d=r[0],h=m(d);if(h||1<f&&"string"==typeof d&&!y.checkClone&&Ae.test(d))return n.each(function(e){var t=n.eq(e);h&&(r[0]=d.call(this,e,t.html())),He(t,r,i,o)});if(f&&(t=(e=xe(r,n[0].ownerDocument,!1,n,o)).firstChild,1===e.childNodes.length&&(e=t),t||o)){for(s=(a=S.map(ve(e,"script"),De)).length;c<f;c++)u=e,c!==p&&(u=S.clone(u,!0,!0),s&&S.merge(a,ve(u,"script"))),i.call(n[c],u,c);if(s)for(l=a[a.length-1].ownerDocument,S.map(a,qe),c=0;c<s;c++)u=a[c],he.test(u.type||"")&&!Y.access(u,"globalEval")&&S.contains(l,u)&&(u.src&&"module"!==(u.type||"").toLowerCase()?S._evalUrl&&!u.noModule&&S._evalUrl(u.src,{nonce:u.nonce||u.getAttribute("nonce")},l):b(u.textContent.replace(Ne,""),u,l))}return n}function Oe(e,t,n){for(var r,i=t?S.filter(t,e):e,o=0;null!=(r=i[o]);o++)n||1!==r.nodeType||S.cleanData(ve(r)),r.parentNode&&(n&&ie(r)&&ye(ve(r,"script")),r.parentNode.removeChild(r));return e}S.extend({htmlPrefilter:function(e){return e},clone:function(e,t,n){var r,i,o,a,s,u,l,c=e.cloneNode(!0),f=ie(e);if(!(y.noCloneChecked||1!==e.nodeType&&11!==e.nodeType||S.isXMLDoc(e)))for(a=ve(c),r=0,i=(o=ve(e)).length;r<i;r++)s=o[r],u=a[r],void 0,"input"===(l=u.nodeName.toLowerCase())&&pe.test(s.type)?u.checked=s.checked:"input"!==l&&"textarea"!==l||(u.defaultValue=s.defaultValue);if(t)if(n)for(o=o||ve(e),a=a||ve(c),r=0,i=o.length;r<i;r++)Le(o[r],a[r]);else Le(e,c);return 0<(a=ve(c,"script")).length&&ye(a,!f&&ve(e,"script")),c},cleanData:function(e){for(var t,n,r,i=S.event.special,o=0;void 0!==(n=e[o]);o++)if(V(n)){if(t=n[Y.expando]){if(t.events)for(r in t.events)i[r]?S.event.remove(n,r):S.removeEvent(n,r,t.handle);n[Y.expando]=void 0}n[Q.expando]&&(n[Q.expando]=void 0)}}}),S.fn.extend({detach:function(e){return Oe(this,e,!0)},remove:function(e){return Oe(this,e)},text:function(e){return $(this,function(e){return void 0===e?S.text(this):this.empty().each(function(){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||(this.textContent=e)})},null,e,arguments.length)},append:function(){return He(this,arguments,function(e){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||je(this,e).appendChild(e)})},prepend:function(){return He(this,arguments,function(e){if(1===this.nodeType||11===this.nodeType||9===this.nodeType){var t=je(this,e);t.insertBefore(e,t.firstChild)}})},before:function(){return He(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this)})},after:function(){return He(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this.nextSibling)})},empty:function(){for(var e,t=0;null!=(e=this[t]);t++)1===e.nodeType&&(S.cleanData(ve(e,!1)),e.textContent="");return this},clone:function(e,t){return e=null!=e&&e,t=null==t?e:t,this.map(function(){return S.clone(this,e,t)})},html:function(e){return $(this,function(e){var t=this[0]||{},n=0,r=this.length;if(void 0===e&&1===t.nodeType)return t.innerHTML;if("string"==typeof e&&!ke.test(e)&&!ge[(de.exec(e)||["",""])[1].toLowerCase()]){e=S.htmlPrefilter(e);try{for(;n<r;n++)1===(t=this[n]||{}).nodeType&&(S.cleanData(ve(t,!1)),t.innerHTML=e);t=0}catch(e){}}t&&this.empty().append(e)},null,e,arguments.length)},replaceWith:function(){var n=[];return He(this,arguments,function(e){var t=this.parentNode;S.inArray(this,n)<0&&(S.cleanData(ve(this)),t&&t.replaceChild(e,this))},n)}}),S.each({appendTo:"append",prependTo:"prepend",insertBefore:"before",insertAfter:"after",replaceAll:"replaceWith"},function(e,a){S.fn[e]=function(e){for(var t,n=[],r=S(e),i=r.length-1,o=0;o<=i;o++)t=o===i?this:this.clone(!0),S(r[o])[a](t),u.apply(n,t.get());return this.pushStack(n)}});var Pe=new RegExp("^("+ee+")(?!px)[a-z%]+$","i"),Re=function(e){var t=e.ownerDocument.defaultView;return t&&t.opener||(t=C),t.getComputedStyle(e)},Me=function(e,t,n){var r,i,o={};for(i in t)o[i]=e.style[i],e.style[i]=t[i];for(i in r=n.call(e),t)e.style[i]=o[i];return r},Ie=new RegExp(ne.join("|"),"i");function We(e,t,n){var r,i,o,a,s=e.style;return(n=n||Re(e))&&(""!==(a=n.getPropertyValue(t)||n[t])||ie(e)||(a=S.style(e,t)),!y.pixelBoxStyles()&&Pe.test(a)&&Ie.test(t)&&(r=s.width,i=s.minWidth,o=s.maxWidth,s.minWidth=s.maxWidth=s.width=a,a=n.width,s.width=r,s.minWidth=i,s.maxWidth=o)),void 0!==a?a+"":a}function Fe(e,t){return{get:function(){if(!e())return(this.get=t).apply(this,arguments);delete this.get}}}!function(){function e(){if(l){u.style.cssText="position:absolute;left:-11111px;width:60px;margin-top:1px;padding:0;border:0",l.style.cssText="position:relative;display:block;box-sizing:border-box;overflow:scroll;margin:auto;border:1px;padding:1px;width:60%;top:1%",re.appendChild(u).appendChild(l);var e=C.getComputedStyle(l);n="1%"!==e.top,s=12===t(e.marginLeft),l.style.right="60%",o=36===t(e.right),r=36===t(e.width),l.style.position="absolute",i=12===t(l.offsetWidth/3),re.removeChild(u),l=null}}function t(e){return Math.round(parseFloat(e))}var n,r,i,o,a,s,u=E.createElement("div"),l=E.createElement("div");l.style&&(l.style.backgroundClip="content-box",l.cloneNode(!0).style.backgroundClip="",y.clearCloneStyle="content-box"===l.style.backgroundClip,S.extend(y,{boxSizingReliable:function(){return e(),r},pixelBoxStyles:function(){return e(),o},pixelPosition:function(){return e(),n},reliableMarginLeft:function(){return e(),s},scrollboxSize:function(){return e(),i},reliableTrDimensions:function(){var e,t,n,r;return null==a&&(e=E.createElement("table"),t=E.createElement("tr"),n=E.createElement("div"),e.style.cssText="position:absolute;left:-11111px;border-collapse:separate",t.style.cssText="border:1px solid",t.style.height="1px",n.style.height="9px",n.style.display="block",re.appendChild(e).appendChild(t).appendChild(n),r=C.getComputedStyle(t),a=parseInt(r.height,10)+parseInt(r.borderTopWidth,10)+parseInt(r.borderBottomWidth,10)===t.offsetHeight,re.removeChild(e)),a}}))}();var Be=["Webkit","Moz","ms"],$e=E.createElement("div").style,_e={};function ze(e){var t=S.cssProps[e]||_e[e];return t||(e in $e?e:_e[e]=function(e){var t=e[0].toUpperCase()+e.slice(1),n=Be.length;while(n--)if((e=Be[n]+t)in $e)return e}(e)||e)}var Ue=/^(none|table(?!-c[ea]).+)/,Xe=/^--/,Ve={position:"absolute",visibility:"hidden",display:"block"},Ge={letterSpacing:"0",fontWeight:"400"};function Ye(e,t,n){var r=te.exec(t);return r?Math.max(0,r[2]-(n||0))+(r[3]||"px"):t}function Qe(e,t,n,r,i,o){var a="width"===t?1:0,s=0,u=0;if(n===(r?"border":"content"))return 0;for(;a<4;a+=2)"margin"===n&&(u+=S.css(e,n+ne[a],!0,i)),r?("content"===n&&(u-=S.css(e,"padding"+ne[a],!0,i)),"margin"!==n&&(u-=S.css(e,"border"+ne[a]+"Width",!0,i))):(u+=S.css(e,"padding"+ne[a],!0,i),"padding"!==n?u+=S.css(e,"border"+ne[a]+"Width",!0,i):s+=S.css(e,"border"+ne[a]+"Width",!0,i));return!r&&0<=o&&(u+=Math.max(0,Math.ceil(e["offset"+t[0].toUpperCase()+t.slice(1)]-o-u-s-.5))||0),u}function Je(e,t,n){var r=Re(e),i=(!y.boxSizingReliable()||n)&&"border-box"===S.css(e,"boxSizing",!1,r),o=i,a=We(e,t,r),s="offset"+t[0].toUpperCase()+t.slice(1);if(Pe.test(a)){if(!n)return a;a="auto"}return(!y.boxSizingReliable()&&i||!y.reliableTrDimensions()&&A(e,"tr")||"auto"===a||!parseFloat(a)&&"inline"===S.css(e,"display",!1,r))&&e.getClientRects().length&&(i="border-box"===S.css(e,"boxSizing",!1,r),(o=s in e)&&(a=e[s])),(a=parseFloat(a)||0)+Qe(e,t,n||(i?"border":"content"),o,r,a)+"px"}function Ke(e,t,n,r,i){return new Ke.prototype.init(e,t,n,r,i)}S.extend({cssHooks:{opacity:{get:function(e,t){if(t){var n=We(e,"opacity");return""===n?"1":n}}}},cssNumber:{animationIterationCount:!0,columnCount:!0,fillOpacity:!0,flexGrow:!0,flexShrink:!0,fontWeight:!0,gridArea:!0,gridColumn:!0,gridColumnEnd:!0,gridColumnStart:!0,gridRow:!0,gridRowEnd:!0,gridRowStart:!0,lineHeight:!0,opacity:!0,order:!0,orphans:!0,widows:!0,zIndex:!0,zoom:!0},cssProps:{},style:function(e,t,n,r){if(e&&3!==e.nodeType&&8!==e.nodeType&&e.style){var i,o,a,s=X(t),u=Xe.test(t),l=e.style;if(u||(t=ze(s)),a=S.cssHooks[t]||S.cssHooks[s],void 0===n)return a&&"get"in a&&void 0!==(i=a.get(e,!1,r))?i:l[t];"string"===(o=typeof n)&&(i=te.exec(n))&&i[1]&&(n=se(e,t,i),o="number"),null!=n&&n==n&&("number"!==o||u||(n+=i&&i[3]||(S.cssNumber[s]?"":"px")),y.clearCloneStyle||""!==n||0!==t.indexOf("background")||(l[t]="inherit"),a&&"set"in a&&void 0===(n=a.set(e,n,r))||(u?l.setProperty(t,n):l[t]=n))}},css:function(e,t,n,r){var i,o,a,s=X(t);return Xe.test(t)||(t=ze(s)),(a=S.cssHooks[t]||S.cssHooks[s])&&"get"in a&&(i=a.get(e,!0,n)),void 0===i&&(i=We(e,t,r)),"normal"===i&&t in Ge&&(i=Ge[t]),""===n||n?(o=parseFloat(i),!0===n||isFinite(o)?o||0:i):i}}),S.each(["height","width"],function(e,u){S.cssHooks[u]={get:function(e,t,n){if(t)return!Ue.test(S.css(e,"display"))||e.getClientRects().length&&e.getBoundingClientRect().width?Je(e,u,n):Me(e,Ve,function(){return Je(e,u,n)})},set:function(e,t,n){var r,i=Re(e),o=!y.scrollboxSize()&&"absolute"===i.position,a=(o||n)&&"border-box"===S.css(e,"boxSizing",!1,i),s=n?Qe(e,u,n,a,i):0;return a&&o&&(s-=Math.ceil(e["offset"+u[0].toUpperCase()+u.slice(1)]-parseFloat(i[u])-Qe(e,u,"border",!1,i)-.5)),s&&(r=te.exec(t))&&"px"!==(r[3]||"px")&&(e.style[u]=t,t=S.css(e,u)),Ye(0,t,s)}}}),S.cssHooks.marginLeft=Fe(y.reliableMarginLeft,function(e,t){if(t)return(parseFloat(We(e,"marginLeft"))||e.getBoundingClientRect().left-Me(e,{marginLeft:0},function(){return e.getBoundingClientRect().left}))+"px"}),S.each({margin:"",padding:"",border:"Width"},function(i,o){S.cssHooks[i+o]={expand:function(e){for(var t=0,n={},r="string"==typeof e?e.split(" "):[e];t<4;t++)n[i+ne[t]+o]=r[t]||r[t-2]||r[0];return n}},"margin"!==i&&(S.cssHooks[i+o].set=Ye)}),S.fn.extend({css:function(e,t){return $(this,function(e,t,n){var r,i,o={},a=0;if(Array.isArray(t)){for(r=Re(e),i=t.length;a<i;a++)o[t[a]]=S.css(e,t[a],!1,r);return o}return void 0!==n?S.style(e,t,n):S.css(e,t)},e,t,1<arguments.length)}}),((S.Tween=Ke).prototype={constructor:Ke,init:function(e,t,n,r,i,o){this.elem=e,this.prop=n,this.easing=i||S.easing._default,this.options=t,this.start=this.now=this.cur(),this.end=r,this.unit=o||(S.cssNumber[n]?"":"px")},cur:function(){var e=Ke.propHooks[this.prop];return e&&e.get?e.get(this):Ke.propHooks._default.get(this)},run:function(e){var t,n=Ke.propHooks[this.prop];return this.options.duration?this.pos=t=S.easing[this.easing](e,this.options.duration*e,0,1,this.options.duration):this.pos=t=e,this.now=(this.end-this.start)*t+this.start,this.options.step&&this.options.step.call(this.elem,this.now,this),n&&n.set?n.set(this):Ke.propHooks._default.set(this),this}}).init.prototype=Ke.prototype,(Ke.propHooks={_default:{get:function(e){var t;return 1!==e.elem.nodeType||null!=e.elem[e.prop]&&null==e.elem.style[e.prop]?e.elem[e.prop]:(t=S.css(e.elem,e.prop,""))&&"auto"!==t?t:0},set:function(e){S.fx.step[e.prop]?S.fx.step[e.prop](e):1!==e.elem.nodeType||!S.cssHooks[e.prop]&&null==e.elem.style[ze(e.prop)]?e.elem[e.prop]=e.now:S.style(e.elem,e.prop,e.now+e.unit)}}}).scrollTop=Ke.propHooks.scrollLeft={set:function(e){e.elem.nodeType&&e.elem.parentNode&&(e.elem[e.prop]=e.now)}},S.easing={linear:function(e){return e},swing:function(e){return.5-Math.cos(e*Math.PI)/2},_default:"swing"},S.fx=Ke.prototype.init,S.fx.step={};var Ze,et,tt,nt,rt=/^(?:toggle|show|hide)$/,it=/queueHooks$/;function ot(){et&&(!1===E.hidden&&C.requestAnimationFrame?C.requestAnimationFrame(ot):C.setTimeout(ot,S.fx.interval),S.fx.tick())}function at(){return C.setTimeout(function(){Ze=void 0}),Ze=Date.now()}function st(e,t){var n,r=0,i={height:e};for(t=t?1:0;r<4;r+=2-t)i["margin"+(n=ne[r])]=i["padding"+n]=e;return t&&(i.opacity=i.width=e),i}function ut(e,t,n){for(var r,i=(lt.tweeners[t]||[]).concat(lt.tweeners["*"]),o=0,a=i.length;o<a;o++)if(r=i[o].call(n,t,e))return r}function lt(o,e,t){var n,a,r=0,i=lt.prefilters.length,s=S.Deferred().always(function(){delete u.elem}),u=function(){if(a)return!1;for(var e=Ze||at(),t=Math.max(0,l.startTime+l.duration-e),n=1-(t/l.duration||0),r=0,i=l.tweens.length;r<i;r++)l.tweens[r].run(n);return s.notifyWith(o,[l,n,t]),n<1&&i?t:(i||s.notifyWith(o,[l,1,0]),s.resolveWith(o,[l]),!1)},l=s.promise({elem:o,props:S.extend({},e),opts:S.extend(!0,{specialEasing:{},easing:S.easing._default},t),originalProperties:e,originalOptions:t,startTime:Ze||at(),duration:t.duration,tweens:[],createTween:function(e,t){var n=S.Tween(o,l.opts,e,t,l.opts.specialEasing[e]||l.opts.easing);return l.tweens.push(n),n},stop:function(e){var t=0,n=e?l.tweens.length:0;if(a)return this;for(a=!0;t<n;t++)l.tweens[t].run(1);return e?(s.notifyWith(o,[l,1,0]),s.resolveWith(o,[l,e])):s.rejectWith(o,[l,e]),this}}),c=l.props;for(!function(e,t){var n,r,i,o,a;for(n in e)if(i=t[r=X(n)],o=e[n],Array.isArray(o)&&(i=o[1],o=e[n]=o[0]),n!==r&&(e[r]=o,delete e[n]),(a=S.cssHooks[r])&&"expand"in a)for(n in o=a.expand(o),delete e[r],o)n in e||(e[n]=o[n],t[n]=i);else t[r]=i}(c,l.opts.specialEasing);r<i;r++)if(n=lt.prefilters[r].call(l,o,c,l.opts))return m(n.stop)&&(S._queueHooks(l.elem,l.opts.queue).stop=n.stop.bind(n)),n;return S.map(c,ut,l),m(l.opts.start)&&l.opts.start.call(o,l),l.progress(l.opts.progress).done(l.opts.done,l.opts.complete).fail(l.opts.fail).always(l.opts.always),S.fx.timer(S.extend(u,{elem:o,anim:l,queue:l.opts.queue})),l}S.Animation=S.extend(lt,{tweeners:{"*":[function(e,t){var n=this.createTween(e,t);return se(n.elem,e,te.exec(t),n),n}]},tweener:function(e,t){m(e)?(t=e,e=["*"]):e=e.match(P);for(var n,r=0,i=e.length;r<i;r++)n=e[r],lt.tweeners[n]=lt.tweeners[n]||[],lt.tweeners[n].unshift(t)},prefilters:[function(e,t,n){var r,i,o,a,s,u,l,c,f="width"in t||"height"in t,p=this,d={},h=e.style,g=e.nodeType&&ae(e),v=Y.get(e,"fxshow");for(r in n.queue||(null==(a=S._queueHooks(e,"fx")).unqueued&&(a.unqueued=0,s=a.empty.fire,a.empty.fire=function(){a.unqueued||s()}),a.unqueued++,p.always(function(){p.always(function(){a.unqueued--,S.queue(e,"fx").length||a.empty.fire()})})),t)if(i=t[r],rt.test(i)){if(delete t[r],o=o||"toggle"===i,i===(g?"hide":"show")){if("show"!==i||!v||void 0===v[r])continue;g=!0}d[r]=v&&v[r]||S.style(e,r)}if((u=!S.isEmptyObject(t))||!S.isEmptyObject(d))for(r in f&&1===e.nodeType&&(n.overflow=[h.overflow,h.overflowX,h.overflowY],null==(l=v&&v.display)&&(l=Y.get(e,"display")),"none"===(c=S.css(e,"display"))&&(l?c=l:(le([e],!0),l=e.style.display||l,c=S.css(e,"display"),le([e]))),("inline"===c||"inline-block"===c&&null!=l)&&"none"===S.css(e,"float")&&(u||(p.done(function(){h.display=l}),null==l&&(c=h.display,l="none"===c?"":c)),h.display="inline-block")),n.overflow&&(h.overflow="hidden",p.always(function(){h.overflow=n.overflow[0],h.overflowX=n.overflow[1],h.overflowY=n.overflow[2]})),u=!1,d)u||(v?"hidden"in v&&(g=v.hidden):v=Y.access(e,"fxshow",{display:l}),o&&(v.hidden=!g),g&&le([e],!0),p.done(function(){for(r in g||le([e]),Y.remove(e,"fxshow"),d)S.style(e,r,d[r])})),u=ut(g?v[r]:0,r,p),r in v||(v[r]=u.start,g&&(u.end=u.start,u.start=0))}],prefilter:function(e,t){t?lt.prefilters.unshift(e):lt.prefilters.push(e)}}),S.speed=function(e,t,n){var r=e&&"object"==typeof e?S.extend({},e):{complete:n||!n&&t||m(e)&&e,duration:e,easing:n&&t||t&&!m(t)&&t};return S.fx.off?r.duration=0:"number"!=typeof r.duration&&(r.duration in S.fx.speeds?r.duration=S.fx.speeds[r.duration]:r.duration=S.fx.speeds._default),null!=r.queue&&!0!==r.queue||(r.queue="fx"),r.old=r.complete,r.complete=function(){m(r.old)&&r.old.call(this),r.queue&&S.dequeue(this,r.queue)},r},S.fn.extend({fadeTo:function(e,t,n,r){return this.filter(ae).css("opacity",0).show().end().animate({opacity:t},e,n,r)},animate:function(t,e,n,r){var i=S.isEmptyObject(t),o=S.speed(e,n,r),a=function(){var e=lt(this,S.extend({},t),o);(i||Y.get(this,"finish"))&&e.stop(!0)};return a.finish=a,i||!1===o.queue?this.each(a):this.queue(o.queue,a)},stop:function(i,e,o){var a=function(e){var t=e.stop;delete e.stop,t(o)};return"string"!=typeof i&&(o=e,e=i,i=void 0),e&&this.queue(i||"fx",[]),this.each(function(){var e=!0,t=null!=i&&i+"queueHooks",n=S.timers,r=Y.get(this);if(t)r[t]&&r[t].stop&&a(r[t]);else for(t in r)r[t]&&r[t].stop&&it.test(t)&&a(r[t]);for(t=n.length;t--;)n[t].elem!==this||null!=i&&n[t].queue!==i||(n[t].anim.stop(o),e=!1,n.splice(t,1));!e&&o||S.dequeue(this,i)})},finish:function(a){return!1!==a&&(a=a||"fx"),this.each(function(){var e,t=Y.get(this),n=t[a+"queue"],r=t[a+"queueHooks"],i=S.timers,o=n?n.length:0;for(t.finish=!0,S.queue(this,a,[]),r&&r.stop&&r.stop.call(this,!0),e=i.length;e--;)i[e].elem===this&&i[e].queue===a&&(i[e].anim.stop(!0),i.splice(e,1));for(e=0;e<o;e++)n[e]&&n[e].finish&&n[e].finish.call(this);delete t.finish})}}),S.each(["toggle","show","hide"],function(e,r){var i=S.fn[r];S.fn[r]=function(e,t,n){return null==e||"boolean"==typeof e?i.apply(this,arguments):this.animate(st(r,!0),e,t,n)}}),S.each({slideDown:st("show"),slideUp:st("hide"),slideToggle:st("toggle"),fadeIn:{opacity:"show"},fadeOut:{opacity:"hide"},fadeToggle:{opacity:"toggle"}},function(e,r){S.fn[e]=function(e,t,n){return this.animate(r,e,t,n)}}),S.timers=[],S.fx.tick=function(){var e,t=0,n=S.timers;for(Ze=Date.now();t<n.length;t++)(e=n[t])()||n[t]!==e||n.splice(t--,1);n.length||S.fx.stop(),Ze=void 0},S.fx.timer=function(e){S.timers.push(e),S.fx.start()},S.fx.interval=13,S.fx.start=function(){et||(et=!0,ot())},S.fx.stop=function(){et=null},S.fx.speeds={slow:600,fast:200,_default:400},S.fn.delay=function(r,e){return r=S.fx&&S.fx.speeds[r]||r,e=e||"fx",this.queue(e,function(e,t){var n=C.setTimeout(e,r);t.stop=function(){C.clearTimeout(n)}})},tt=E.createElement("input"),nt=E.createElement("select").appendChild(E.createElement("option")),tt.type="checkbox",y.checkOn=""!==tt.value,y.optSelected=nt.selected,(tt=E.createElement("input")).value="t",tt.type="radio",y.radioValue="t"===tt.value;var ct,ft=S.expr.attrHandle;S.fn.extend({attr:function(e,t){return $(this,S.attr,e,t,1<arguments.length)},removeAttr:function(e){return this.each(function(){S.removeAttr(this,e)})}}),S.extend({attr:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return"undefined"==typeof e.getAttribute?S.prop(e,t,n):(1===o&&S.isXMLDoc(e)||(i=S.attrHooks[t.toLowerCase()]||(S.expr.match.bool.test(t)?ct:void 0)),void 0!==n?null===n?void S.removeAttr(e,t):i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:(e.setAttribute(t,n+""),n):i&&"get"in i&&null!==(r=i.get(e,t))?r:null==(r=S.find.attr(e,t))?void 0:r)},attrHooks:{type:{set:function(e,t){if(!y.radioValue&&"radio"===t&&A(e,"input")){var n=e.value;return e.setAttribute("type",t),n&&(e.value=n),t}}}},removeAttr:function(e,t){var n,r=0,i=t&&t.match(P);if(i&&1===e.nodeType)while(n=i[r++])e.removeAttribute(n)}}),ct={set:function(e,t,n){return!1===t?S.removeAttr(e,n):e.setAttribute(n,n),n}},S.each(S.expr.match.bool.source.match(/\w+/g),function(e,t){var a=ft[t]||S.find.attr;ft[t]=function(e,t,n){var r,i,o=t.toLowerCase();return n||(i=ft[o],ft[o]=r,r=null!=a(e,t,n)?o:null,ft[o]=i),r}});var pt=/^(?:input|select|textarea|button)$/i,dt=/^(?:a|area)$/i;function ht(e){return(e.match(P)||[]).join(" ")}function gt(e){return e.getAttribute&&e.getAttribute("class")||""}function vt(e){return Array.isArray(e)?e:"string"==typeof e&&e.match(P)||[]}S.fn.extend({prop:function(e,t){return $(this,S.prop,e,t,1<arguments.length)},removeProp:function(e){return this.each(function(){delete this[S.propFix[e]||e]})}}),S.extend({prop:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return 1===o&&S.isXMLDoc(e)||(t=S.propFix[t]||t,i=S.propHooks[t]),void 0!==n?i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:e[t]=n:i&&"get"in i&&null!==(r=i.get(e,t))?r:e[t]},propHooks:{tabIndex:{get:function(e){var t=S.find.attr(e,"tabindex");return t?parseInt(t,10):pt.test(e.nodeName)||dt.test(e.nodeName)&&e.href?0:-1}}},propFix:{"for":"htmlFor","class":"className"}}),y.optSelected||(S.propHooks.selected={get:function(e){var t=e.parentNode;return t&&t.parentNode&&t.parentNode.selectedIndex,null},set:function(e){var t=e.parentNode;t&&(t.selectedIndex,t.parentNode&&t.parentNode.selectedIndex)}}),S.each(["tabIndex","readOnly","maxLength","cellSpacing","cellPadding","rowSpan","colSpan","useMap","frameBorder","contentEditable"],function(){S.propFix[this.toLowerCase()]=this}),S.fn.extend({addClass:function(t){var e,n,r,i,o,a,s,u=0;if(m(t))return this.each(function(e){S(this).addClass(t.call(this,e,gt(this)))});if((e=vt(t)).length)while(n=this[u++])if(i=gt(n),r=1===n.nodeType&&" "+ht(i)+" "){a=0;while(o=e[a++])r.indexOf(" "+o+" ")<0&&(r+=o+" ");i!==(s=ht(r))&&n.setAttribute("class",s)}return this},removeClass:function(t){var e,n,r,i,o,a,s,u=0;if(m(t))return this.each(function(e){S(this).removeClass(t.call(this,e,gt(this)))});if(!arguments.length)return this.attr("class","");if((e=vt(t)).length)while(n=this[u++])if(i=gt(n),r=1===n.nodeType&&" "+ht(i)+" "){a=0;while(o=e[a++])while(-1<r.indexOf(" "+o+" "))r=r.replace(" "+o+" "," ");i!==(s=ht(r))&&n.setAttribute("class",s)}return this},toggleClass:function(i,t){var o=typeof i,a="string"===o||Array.isArray(i);return"boolean"==typeof t&&a?t?this.addClass(i):this.removeClass(i):m(i)?this.each(function(e){S(this).toggleClass(i.call(this,e,gt(this),t),t)}):this.each(function(){var e,t,n,r;if(a){t=0,n=S(this),r=vt(i);while(e=r[t++])n.hasClass(e)?n.removeClass(e):n.addClass(e)}else void 0!==i&&"boolean"!==o||((e=gt(this))&&Y.set(this,"__className__",e),this.setAttribute&&this.setAttribute("class",e||!1===i?"":Y.get(this,"__className__")||""))})},hasClass:function(e){var t,n,r=0;t=" "+e+" ";while(n=this[r++])if(1===n.nodeType&&-1<(" "+ht(gt(n))+" ").indexOf(t))return!0;return!1}});var yt=/\r/g;S.fn.extend({val:function(n){var r,e,i,t=this[0];return arguments.length?(i=m(n),this.each(function(e){var t;1===this.nodeType&&(null==(t=i?n.call(this,e,S(this).val()):n)?t="":"number"==typeof t?t+="":Array.isArray(t)&&(t=S.map(t,function(e){return null==e?"":e+""})),(r=S.valHooks[this.type]||S.valHooks[this.nodeName.toLowerCase()])&&"set"in r&&void 0!==r.set(this,t,"value")||(this.value=t))})):t?(r=S.valHooks[t.type]||S.valHooks[t.nodeName.toLowerCase()])&&"get"in r&&void 0!==(e=r.get(t,"value"))?e:"string"==typeof(e=t.value)?e.replace(yt,""):null==e?"":e:void 0}}),S.extend({valHooks:{option:{get:function(e){var t=S.find.attr(e,"value");return null!=t?t:ht(S.text(e))}},select:{get:function(e){var t,n,r,i=e.options,o=e.selectedIndex,a="select-one"===e.type,s=a?null:[],u=a?o+1:i.length;for(r=o<0?u:a?o:0;r<u;r++)if(((n=i[r]).selected||r===o)&&!n.disabled&&(!n.parentNode.disabled||!A(n.parentNode,"optgroup"))){if(t=S(n).val(),a)return t;s.push(t)}return s},set:function(e,t){var n,r,i=e.options,o=S.makeArray(t),a=i.length;while(a--)((r=i[a]).selected=-1<S.inArray(S.valHooks.option.get(r),o))&&(n=!0);return n||(e.selectedIndex=-1),o}}}}),S.each(["radio","checkbox"],function(){S.valHooks[this]={set:function(e,t){if(Array.isArray(t))return e.checked=-1<S.inArray(S(e).val(),t)}},y.checkOn||(S.valHooks[this].get=function(e){return null===e.getAttribute("value")?"on":e.value})}),y.focusin="onfocusin"in C;var mt=/^(?:focusinfocus|focusoutblur)$/,xt=function(e){e.stopPropagation()};S.extend(S.event,{trigger:function(e,t,n,r){var i,o,a,s,u,l,c,f,p=[n||E],d=v.call(e,"type")?e.type:e,h=v.call(e,"namespace")?e.namespace.split("."):[];if(o=f=a=n=n||E,3!==n.nodeType&&8!==n.nodeType&&!mt.test(d+S.event.triggered)&&(-1<d.indexOf(".")&&(d=(h=d.split(".")).shift(),h.sort()),u=d.indexOf(":")<0&&"on"+d,(e=e[S.expando]?e:new S.Event(d,"object"==typeof e&&e)).isTrigger=r?2:3,e.namespace=h.join("."),e.rnamespace=e.namespace?new RegExp("(^|\\.)"+h.join("\\.(?:.*\\.|)")+"(\\.|$)"):null,e.result=void 0,e.target||(e.target=n),t=null==t?[e]:S.makeArray(t,[e]),c=S.event.special[d]||{},r||!c.trigger||!1!==c.trigger.apply(n,t))){if(!r&&!c.noBubble&&!x(n)){for(s=c.delegateType||d,mt.test(s+d)||(o=o.parentNode);o;o=o.parentNode)p.push(o),a=o;a===(n.ownerDocument||E)&&p.push(a.defaultView||a.parentWindow||C)}i=0;while((o=p[i++])&&!e.isPropagationStopped())f=o,e.type=1<i?s:c.bindType||d,(l=(Y.get(o,"events")||Object.create(null))[e.type]&&Y.get(o,"handle"))&&l.apply(o,t),(l=u&&o[u])&&l.apply&&V(o)&&(e.result=l.apply(o,t),!1===e.result&&e.preventDefault());return e.type=d,r||e.isDefaultPrevented()||c._default&&!1!==c._default.apply(p.pop(),t)||!V(n)||u&&m(n[d])&&!x(n)&&((a=n[u])&&(n[u]=null),S.event.triggered=d,e.isPropagationStopped()&&f.addEventListener(d,xt),n[d](),e.isPropagationStopped()&&f.removeEventListener(d,xt),S.event.triggered=void 0,a&&(n[u]=a)),e.result}},simulate:function(e,t,n){var r=S.extend(new S.Event,n,{type:e,isSimulated:!0});S.event.trigger(r,null,t)}}),S.fn.extend({trigger:function(e,t){return this.each(function(){S.event.trigger(e,t,this)})},triggerHandler:function(e,t){var n=this[0];if(n)return S.event.trigger(e,t,n,!0)}}),y.focusin||S.each({focus:"focusin",blur:"focusout"},function(n,r){var i=function(e){S.event.simulate(r,e.target,S.event.fix(e))};S.event.special[r]={setup:function(){var e=this.ownerDocument||this.document||this,t=Y.access(e,r);t||e.addEventListener(n,i,!0),Y.access(e,r,(t||0)+1)},teardown:function(){var e=this.ownerDocument||this.document||this,t=Y.access(e,r)-1;t?Y.access(e,r,t):(e.removeEventListener(n,i,!0),Y.remove(e,r))}}});var bt=C.location,wt={guid:Date.now()},Tt=/\?/;S.parseXML=function(e){var t,n;if(!e||"string"!=typeof e)return null;try{t=(new C.DOMParser).parseFromString(e,"text/xml")}catch(e){}return n=t&&t.getElementsByTagName("parsererror")[0],t&&!n||S.error("Invalid XML: "+(n?S.map(n.childNodes,function(e){return e.textContent}).join("\n"):e)),t};var Ct=/\[\]$/,Et=/\r?\n/g,St=/^(?:submit|button|image|reset|file)$/i,kt=/^(?:input|select|textarea|keygen)/i;function At(n,e,r,i){var t;if(Array.isArray(e))S.each(e,function(e,t){r||Ct.test(n)?i(n,t):At(n+"["+("object"==typeof t&&null!=t?e:"")+"]",t,r,i)});else if(r||"object"!==w(e))i(n,e);else for(t in e)At(n+"["+t+"]",e[t],r,i)}S.param=function(e,t){var n,r=[],i=function(e,t){var n=m(t)?t():t;r[r.length]=encodeURIComponent(e)+"="+encodeURIComponent(null==n?"":n)};if(null==e)return"";if(Array.isArray(e)||e.jquery&&!S.isPlainObject(e))S.each(e,function(){i(this.name,this.value)});else for(n in e)At(n,e[n],t,i);return r.join("&")},S.fn.extend({serialize:function(){return S.param(this.serializeArray())},serializeArray:function(){return this.map(function(){var e=S.prop(this,"elements");return e?S.makeArray(e):this}).filter(function(){var e=this.type;return this.name&&!S(this).is(":disabled")&&kt.test(this.nodeName)&&!St.test(e)&&(this.checked||!pe.test(e))}).map(function(e,t){var n=S(this).val();return null==n?null:Array.isArray(n)?S.map(n,function(e){return{name:t.name,value:e.replace(Et,"\r\n")}}):{name:t.name,value:n.replace(Et,"\r\n")}}).get()}});var Nt=/%20/g,jt=/#.*$/,Dt=/([?&])_=[^&]*/,qt=/^(.*?):[ \t]*([^\r\n]*)$/gm,Lt=/^(?:GET|HEAD)$/,Ht=/^\/\//,Ot={},Pt={},Rt="*/".concat("*"),Mt=E.createElement("a");function It(o){return function(e,t){"string"!=typeof e&&(t=e,e="*");var n,r=0,i=e.toLowerCase().match(P)||[];if(m(t))while(n=i[r++])"+"===n[0]?(n=n.slice(1)||"*",(o[n]=o[n]||[]).unshift(t)):(o[n]=o[n]||[]).push(t)}}function Wt(t,i,o,a){var s={},u=t===Pt;function l(e){var r;return s[e]=!0,S.each(t[e]||[],function(e,t){var n=t(i,o,a);return"string"!=typeof n||u||s[n]?u?!(r=n):void 0:(i.dataTypes.unshift(n),l(n),!1)}),r}return l(i.dataTypes[0])||!s["*"]&&l("*")}function Ft(e,t){var n,r,i=S.ajaxSettings.flatOptions||{};for(n in t)void 0!==t[n]&&((i[n]?e:r||(r={}))[n]=t[n]);return r&&S.extend(!0,e,r),e}Mt.href=bt.href,S.extend({active:0,lastModified:{},etag:{},ajaxSettings:{url:bt.href,type:"GET",isLocal:/^(?:about|app|app-storage|.+-extension|file|res|widget):$/.test(bt.protocol),global:!0,processData:!0,async:!0,contentType:"application/x-www-form-urlencoded; charset=UTF-8",accepts:{"*":Rt,text:"text/plain",html:"text/html",xml:"application/xml, text/xml",json:"application/json, text/javascript"},contents:{xml:/\bxml\b/,html:/\bhtml/,json:/\bjson\b/},responseFields:{xml:"responseXML",text:"responseText",json:"responseJSON"},converters:{"* text":String,"text html":!0,"text json":JSON.parse,"text xml":S.parseXML},flatOptions:{url:!0,context:!0}},ajaxSetup:function(e,t){return t?Ft(Ft(e,S.ajaxSettings),t):Ft(S.ajaxSettings,e)},ajaxPrefilter:It(Ot),ajaxTransport:It(Pt),ajax:function(e,t){"object"==typeof e&&(t=e,e=void 0),t=t||{};var c,f,p,n,d,r,h,g,i,o,v=S.ajaxSetup({},t),y=v.context||v,m=v.context&&(y.nodeType||y.jquery)?S(y):S.event,x=S.Deferred(),b=S.Callbacks("once memory"),w=v.statusCode||{},a={},s={},u="canceled",T={readyState:0,getResponseHeader:function(e){var t;if(h){if(!n){n={};while(t=qt.exec(p))n[t[1].toLowerCase()+" "]=(n[t[1].toLowerCase()+" "]||[]).concat(t[2])}t=n[e.toLowerCase()+" "]}return null==t?null:t.join(", ")},getAllResponseHeaders:function(){return h?p:null},setRequestHeader:function(e,t){return null==h&&(e=s[e.toLowerCase()]=s[e.toLowerCase()]||e,a[e]=t),this},overrideMimeType:function(e){return null==h&&(v.mimeType=e),this},statusCode:function(e){var t;if(e)if(h)T.always(e[T.status]);else for(t in e)w[t]=[w[t],e[t]];return this},abort:function(e){var t=e||u;return c&&c.abort(t),l(0,t),this}};if(x.promise(T),v.url=((e||v.url||bt.href)+"").replace(Ht,bt.protocol+"//"),v.type=t.method||t.type||v.method||v.type,v.dataTypes=(v.dataType||"*").toLowerCase().match(P)||[""],null==v.crossDomain){r=E.createElement("a");try{r.href=v.url,r.href=r.href,v.crossDomain=Mt.protocol+"//"+Mt.host!=r.protocol+"//"+r.host}catch(e){v.crossDomain=!0}}if(v.data&&v.processData&&"string"!=typeof v.data&&(v.data=S.param(v.data,v.traditional)),Wt(Ot,v,t,T),h)return T;for(i in(g=S.event&&v.global)&&0==S.active++&&S.event.trigger("ajaxStart"),v.type=v.type.toUpperCase(),v.hasContent=!Lt.test(v.type),f=v.url.replace(jt,""),v.hasContent?v.data&&v.processData&&0===(v.contentType||"").indexOf("application/x-www-form-urlencoded")&&(v.data=v.data.replace(Nt,"+")):(o=v.url.slice(f.length),v.data&&(v.processData||"string"==typeof v.data)&&(f+=(Tt.test(f)?"&":"?")+v.data,delete v.data),!1===v.cache&&(f=f.replace(Dt,"$1"),o=(Tt.test(f)?"&":"?")+"_="+wt.guid+++o),v.url=f+o),v.ifModified&&(S.lastModified[f]&&T.setRequestHeader("If-Modified-Since",S.lastModified[f]),S.etag[f]&&T.setRequestHeader("If-None-Match",S.etag[f])),(v.data&&v.hasContent&&!1!==v.contentType||t.contentType)&&T.setRequestHeader("Content-Type",v.contentType),T.setRequestHeader("Accept",v.dataTypes[0]&&v.accepts[v.dataTypes[0]]?v.accepts[v.dataTypes[0]]+("*"!==v.dataTypes[0]?", "+Rt+"; q=0.01":""):v.accepts["*"]),v.headers)T.setRequestHeader(i,v.headers[i]);if(v.beforeSend&&(!1===v.beforeSend.call(y,T,v)||h))return T.abort();if(u="abort",b.add(v.complete),T.done(v.success),T.fail(v.error),c=Wt(Pt,v,t,T)){if(T.readyState=1,g&&m.trigger("ajaxSend",[T,v]),h)return T;v.async&&0<v.timeout&&(d=C.setTimeout(function(){T.abort("timeout")},v.timeout));try{h=!1,c.send(a,l)}catch(e){if(h)throw e;l(-1,e)}}else l(-1,"No Transport");function l(e,t,n,r){var i,o,a,s,u,l=t;h||(h=!0,d&&C.clearTimeout(d),c=void 0,p=r||"",T.readyState=0<e?4:0,i=200<=e&&e<300||304===e,n&&(s=function(e,t,n){var r,i,o,a,s=e.contents,u=e.dataTypes;while("*"===u[0])u.shift(),void 0===r&&(r=e.mimeType||t.getResponseHeader("Content-Type"));if(r)for(i in s)if(s[i]&&s[i].test(r)){u.unshift(i);break}if(u[0]in n)o=u[0];else{for(i in n){if(!u[0]||e.converters[i+" "+u[0]]){o=i;break}a||(a=i)}o=o||a}if(o)return o!==u[0]&&u.unshift(o),n[o]}(v,T,n)),!i&&-1<S.inArray("script",v.dataTypes)&&S.inArray("json",v.dataTypes)<0&&(v.converters["text script"]=function(){}),s=function(e,t,n,r){var i,o,a,s,u,l={},c=e.dataTypes.slice();if(c[1])for(a in e.converters)l[a.toLowerCase()]=e.converters[a];o=c.shift();while(o)if(e.responseFields[o]&&(n[e.responseFields[o]]=t),!u&&r&&e.dataFilter&&(t=e.dataFilter(t,e.dataType)),u=o,o=c.shift())if("*"===o)o=u;else if("*"!==u&&u!==o){if(!(a=l[u+" "+o]||l["* "+o]))for(i in l)if((s=i.split(" "))[1]===o&&(a=l[u+" "+s[0]]||l["* "+s[0]])){!0===a?a=l[i]:!0!==l[i]&&(o=s[0],c.unshift(s[1]));break}if(!0!==a)if(a&&e["throws"])t=a(t);else try{t=a(t)}catch(e){return{state:"parsererror",error:a?e:"No conversion from "+u+" to "+o}}}return{state:"success",data:t}}(v,s,T,i),i?(v.ifModified&&((u=T.getResponseHeader("Last-Modified"))&&(S.lastModified[f]=u),(u=T.getResponseHeader("etag"))&&(S.etag[f]=u)),204===e||"HEAD"===v.type?l="nocontent":304===e?l="notmodified":(l=s.state,o=s.data,i=!(a=s.error))):(a=l,!e&&l||(l="error",e<0&&(e=0))),T.status=e,T.statusText=(t||l)+"",i?x.resolveWith(y,[o,l,T]):x.rejectWith(y,[T,l,a]),T.statusCode(w),w=void 0,g&&m.trigger(i?"ajaxSuccess":"ajaxError",[T,v,i?o:a]),b.fireWith(y,[T,l]),g&&(m.trigger("ajaxComplete",[T,v]),--S.active||S.event.trigger("ajaxStop")))}return T},getJSON:function(e,t,n){return S.get(e,t,n,"json")},getScript:function(e,t){return S.get(e,void 0,t,"script")}}),S.each(["get","post"],function(e,i){S[i]=function(e,t,n,r){return m(t)&&(r=r||n,n=t,t=void 0),S.ajax(S.extend({url:e,type:i,dataType:r,data:t,success:n},S.isPlainObject(e)&&e))}}),S.ajaxPrefilter(function(e){var t;for(t in e.headers)"content-type"===t.toLowerCase()&&(e.contentType=e.headers[t]||"")}),S._evalUrl=function(e,t,n){return S.ajax({url:e,type:"GET",dataType:"script",cache:!0,async:!1,global:!1,converters:{"text script":function(){}},dataFilter:function(e){S.globalEval(e,t,n)}})},S.fn.extend({wrapAll:function(e){var t;return this[0]&&(m(e)&&(e=e.call(this[0])),t=S(e,this[0].ownerDocument).eq(0).clone(!0),this[0].parentNode&&t.insertBefore(this[0]),t.map(function(){var e=this;while(e.firstElementChild)e=e.firstElementChild;return e}).append(this)),this},wrapInner:function(n){return m(n)?this.each(function(e){S(this).wrapInner(n.call(this,e))}):this.each(function(){var e=S(this),t=e.contents();t.length?t.wrapAll(n):e.append(n)})},wrap:function(t){var n=m(t);return this.each(function(e){S(this).wrapAll(n?t.call(this,e):t)})},unwrap:function(e){return this.parent(e).not("body").each(function(){S(this).replaceWith(this.childNodes)}),this}}),S.expr.pseudos.hidden=function(e){return!S.expr.pseudos.visible(e)},S.expr.pseudos.visible=function(e){return!!(e.offsetWidth||e.offsetHeight||e.getClientRects().length)},S.ajaxSettings.xhr=function(){try{return new C.XMLHttpRequest}catch(e){}};var Bt={0:200,1223:204},$t=S.ajaxSettings.xhr();y.cors=!!$t&&"withCredentials"in $t,y.ajax=$t=!!$t,S.ajaxTransport(function(i){var o,a;if(y.cors||$t&&!i.crossDomain)return{send:function(e,t){var n,r=i.xhr();if(r.open(i.type,i.url,i.async,i.username,i.password),i.xhrFields)for(n in i.xhrFields)r[n]=i.xhrFields[n];for(n in i.mimeType&&r.overrideMimeType&&r.overrideMimeType(i.mimeType),i.crossDomain||e["X-Requested-With"]||(e["X-Requested-With"]="XMLHttpRequest"),e)r.setRequestHeader(n,e[n]);o=function(e){return function(){o&&(o=a=r.onload=r.onerror=r.onabort=r.ontimeout=r.onreadystatechange=null,"abort"===e?r.abort():"error"===e?"number"!=typeof r.status?t(0,"error"):t(r.status,r.statusText):t(Bt[r.status]||r.status,r.statusText,"text"!==(r.responseType||"text")||"string"!=typeof r.responseText?{binary:r.response}:{text:r.responseText},r.getAllResponseHeaders()))}},r.onload=o(),a=r.onerror=r.ontimeout=o("error"),void 0!==r.onabort?r.onabort=a:r.onreadystatechange=function(){4===r.readyState&&C.setTimeout(function(){o&&a()})},o=o("abort");try{r.send(i.hasContent&&i.data||null)}catch(e){if(o)throw e}},abort:function(){o&&o()}}}),S.ajaxPrefilter(function(e){e.crossDomain&&(e.contents.script=!1)}),S.ajaxSetup({accepts:{script:"text/javascript, application/javascript, application/ecmascript, application/x-ecmascript"},contents:{script:/\b(?:java|ecma)script\b/},converters:{"text script":function(e){return S.globalEval(e),e}}}),S.ajaxPrefilter("script",function(e){void 0===e.cache&&(e.cache=!1),e.crossDomain&&(e.type="GET")}),S.ajaxTransport("script",function(n){var r,i;if(n.crossDomain||n.scriptAttrs)return{send:function(e,t){r=S("<script>").attr(n.scriptAttrs||{}).prop({charset:n.scriptCharset,src:n.url}).on("load error",i=function(e){r.remove(),i=null,e&&t("error"===e.type?404:200,e.type)}),E.head.appendChild(r[0])},abort:function(){i&&i()}}});var _t,zt=[],Ut=/(=)\?(?=&|$)|\?\?/;S.ajaxSetup({jsonp:"callback",jsonpCallback:function(){var e=zt.pop()||S.expando+"_"+wt.guid++;return this[e]=!0,e}}),S.ajaxPrefilter("json jsonp",function(e,t,n){var r,i,o,a=!1!==e.jsonp&&(Ut.test(e.url)?"url":"string"==typeof e.data&&0===(e.contentType||"").indexOf("application/x-www-form-urlencoded")&&Ut.test(e.data)&&"data");if(a||"jsonp"===e.dataTypes[0])return r=e.jsonpCallback=m(e.jsonpCallback)?e.jsonpCallback():e.jsonpCallback,a?e[a]=e[a].replace(Ut,"$1"+r):!1!==e.jsonp&&(e.url+=(Tt.test(e.url)?"&":"?")+e.jsonp+"="+r),e.converters["script json"]=function(){return o||S.error(r+" was not called"),o[0]},e.dataTypes[0]="json",i=C[r],C[r]=function(){o=arguments},n.always(function(){void 0===i?S(C).removeProp(r):C[r]=i,e[r]&&(e.jsonpCallback=t.jsonpCallback,zt.push(r)),o&&m(i)&&i(o[0]),o=i=void 0}),"script"}),y.createHTMLDocument=((_t=E.implementation.createHTMLDocument("").body).innerHTML="<form></form><form></form>",2===_t.childNodes.length),S.parseHTML=function(e,t,n){return"string"!=typeof e?[]:("boolean"==typeof t&&(n=t,t=!1),t||(y.createHTMLDocument?((r=(t=E.implementation.createHTMLDocument("")).createElement("base")).href=E.location.href,t.head.appendChild(r)):t=E),o=!n&&[],(i=N.exec(e))?[t.createElement(i[1])]:(i=xe([e],t,o),o&&o.length&&S(o).remove(),S.merge([],i.childNodes)));var r,i,o},S.fn.load=function(e,t,n){var r,i,o,a=this,s=e.indexOf(" ");return-1<s&&(r=ht(e.slice(s)),e=e.slice(0,s)),m(t)?(n=t,t=void 0):t&&"object"==typeof t&&(i="POST"),0<a.length&&S.ajax({url:e,type:i||"GET",dataType:"html",data:t}).done(function(e){o=arguments,a.html(r?S("<div>").append(S.parseHTML(e)).find(r):e)}).always(n&&function(e,t){a.each(function(){n.apply(this,o||[e.responseText,t,e])})}),this},S.expr.pseudos.animated=function(t){return S.grep(S.timers,function(e){return t===e.elem}).length},S.offset={setOffset:function(e,t,n){var r,i,o,a,s,u,l=S.css(e,"position"),c=S(e),f={};"static"===l&&(e.style.position="relative"),s=c.offset(),o=S.css(e,"top"),u=S.css(e,"left"),("absolute"===l||"fixed"===l)&&-1<(o+u).indexOf("auto")?(a=(r=c.position()).top,i=r.left):(a=parseFloat(o)||0,i=parseFloat(u)||0),m(t)&&(t=t.call(e,n,S.extend({},s))),null!=t.top&&(f.top=t.top-s.top+a),null!=t.left&&(f.left=t.left-s.left+i),"using"in t?t.using.call(e,f):c.css(f)}},S.fn.extend({offset:function(t){if(arguments.length)return void 0===t?this:this.each(function(e){S.offset.setOffset(this,t,e)});var e,n,r=this[0];return r?r.getClientRects().length?(e=r.getBoundingClientRect(),n=r.ownerDocument.defaultView,{top:e.top+n.pageYOffset,left:e.left+n.pageXOffset}):{top:0,left:0}:void 0},position:function(){if(this[0]){var e,t,n,r=this[0],i={top:0,left:0};if("fixed"===S.css(r,"position"))t=r.getBoundingClientRect();else{t=this.offset(),n=r.ownerDocument,e=r.offsetParent||n.documentElement;while(e&&(e===n.body||e===n.documentElement)&&"static"===S.css(e,"position"))e=e.parentNode;e&&e!==r&&1===e.nodeType&&((i=S(e).offset()).top+=S.css(e,"borderTopWidth",!0),i.left+=S.css(e,"borderLeftWidth",!0))}return{top:t.top-i.top-S.css(r,"marginTop",!0),left:t.left-i.left-S.css(r,"marginLeft",!0)}}},offsetParent:function(){return this.map(function(){var e=this.offsetParent;while(e&&"static"===S.css(e,"position"))e=e.offsetParent;return e||re})}}),S.each({scrollLeft:"pageXOffset",scrollTop:"pageYOffset"},function(t,i){var o="pageYOffset"===i;S.fn[t]=function(e){return $(this,function(e,t,n){var r;if(x(e)?r=e:9===e.nodeType&&(r=e.defaultView),void 0===n)return r?r[i]:e[t];r?r.scrollTo(o?r.pageXOffset:n,o?n:r.pageYOffset):e[t]=n},t,e,arguments.length)}}),S.each(["top","left"],function(e,n){S.cssHooks[n]=Fe(y.pixelPosition,function(e,t){if(t)return t=We(e,n),Pe.test(t)?S(e).position()[n]+"px":t})}),S.each({Height:"height",Width:"width"},function(a,s){S.each({padding:"inner"+a,content:s,"":"outer"+a},function(r,o){S.fn[o]=function(e,t){var n=arguments.length&&(r||"boolean"!=typeof e),i=r||(!0===e||!0===t?"margin":"border");return $(this,function(e,t,n){var r;return x(e)?0===o.indexOf("outer")?e["inner"+a]:e.document.documentElement["client"+a]:9===e.nodeType?(r=e.documentElement,Math.max(e.body["scroll"+a],r["scroll"+a],e.body["offset"+a],r["offset"+a],r["client"+a])):void 0===n?S.css(e,t,i):S.style(e,t,n,i)},s,n?e:void 0,n)}})}),S.each(["ajaxStart","ajaxStop","ajaxComplete","ajaxError","ajaxSuccess","ajaxSend"],function(e,t){S.fn[t]=function(e){return this.on(t,e)}}),S.fn.extend({bind:function(e,t,n){return this.on(e,null,t,n)},unbind:function(e,t){return this.off(e,null,t)},delegate:function(e,t,n,r){return this.on(t,e,n,r)},undelegate:function(e,t,n){return 1===arguments.length?this.off(e,"**"):this.off(t,e||"**",n)},hover:function(e,t){return this.mouseenter(e).mouseleave(t||e)}}),S.each("blur focus focusin focusout resize scroll click dblclick mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave change select submit keydown keypress keyup contextmenu".split(" "),function(e,n){S.fn[n]=function(e,t){return 0<arguments.length?this.on(n,null,e,t):this.trigger(n)}});var Xt=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;S.proxy=function(e,t){var n,r,i;if("string"==typeof t&&(n=e[t],t=e,e=n),m(e))return r=s.call(arguments,2),(i=function(){return e.apply(t||this,r.concat(s.call(arguments)))}).guid=e.guid=e.guid||S.guid++,i},S.holdReady=function(e){e?S.readyWait++:S.ready(!0)},S.isArray=Array.isArray,S.parseJSON=JSON.parse,S.nodeName=A,S.isFunction=m,S.isWindow=x,S.camelCase=X,S.type=w,S.now=Date.now,S.isNumeric=function(e){var t=S.type(e);return("number"===t||"string"===t)&&!isNaN(e-parseFloat(e))},S.trim=function(e){return null==e?"":(e+"").replace(Xt,"")},"function"==typeof define&&define.amd&&define("jquery",[],function(){return S});var Vt=C.jQuery,Gt=C.$;return S.noConflict=function(e){return C.$===S&&(C.$=Gt),e&&C.jQuery===S&&(C.jQuery=Vt),S},"undefined"==typeof e&&(C.jQuery=C.$=S),S});
</script>
<meta name="viewport" content="width=device-width, initial-scale=1" />
<style type="text/css">html{font-family:sans-serif;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}body{margin:0}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:1px dotted}b,strong{font-weight:700}dfn{font-style:italic}h1{margin:.67em 0;font-size:2em}mark{color:#000;background:#ff0}small{font-size:80%}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}img{border:0}svg:not(:root){overflow:hidden}figure{margin:1em 40px}hr{height:0;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}pre{overflow:auto}code,kbd,pre,samp{font-family:monospace,monospace;font-size:1em}button,input,optgroup,select,textarea{margin:0;font:inherit;color:inherit}button{overflow:visible}button,select{text-transform:none}button,html input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}input{line-height:normal}input[type=checkbox],input[type=radio]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;padding:0}input[type=number]::-webkit-inner-spin-button,input[type=number]::-webkit-outer-spin-button{height:auto}input[type=search]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}fieldset{padding:.35em .625em .75em;margin:0 2px;border:1px solid silver}legend{padding:0;border:0}textarea{overflow:auto}optgroup{font-weight:700}table{border-spacing:0;border-collapse:collapse}td,th{padding:0}@media print{*,:after,:before{color:#000!important;text-shadow:none!important;background:0 0!important;-webkit-box-shadow:none!important;box-shadow:none!important}a,a:visited{text-decoration:underline}a[href]:after{content:" (" attr(href) ")"}abbr[title]:after{content:" (" attr(title) ")"}a[href^="javascript:"]:after,a[href^="#"]:after{content:""}blockquote,pre{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}img,tr{page-break-inside:avoid}img{max-width:100%!important}h2,h3,p{orphans:3;widows:3}h2,h3{page-break-after:avoid}.navbar{display:none}.btn>.caret,.dropup>.btn>.caret{border-top-color:#000!important}.label{border:1px solid #000}.table{border-collapse:collapse!important}.table td,.table th{background-color:#fff!important}.table-bordered td,.table-bordered th{border:1px solid #ddd!important}}@font-face{font-family:'Glyphicons Halflings';src:url(data:application/vnd.ms-fontobject;base64,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);src:url(data:application/vnd.ms-fontobject;base64,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) format('embedded-opentype'),url(data:font/woff;base64,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) format('woff'),url(data:font/ttf;base64,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) format('truetype'),url(data:image/svg+xml;base64,<?xml version="1.0" standalone="no"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
<svg xmlns="http://www.w3.org/2000/svg">
<metadata></metadata>
<defs>
<font id="glyphicons_halflingsregular" horiz-adv-x="1200" >
<font-face units-per-em="1200" ascent="960" descent="-240" />
<missing-glyph horiz-adv-x="500" />
<glyph horiz-adv-x="0" />
<glyph horiz-adv-x="400" />
<glyph unicode=" " />
<glyph unicode="*" d="M600 1100q15 0 34 -1.5t30 -3.5l11 -1q10 -2 17.5 -10.5t7.5 -18.5v-224l158 158q7 7 18 8t19 -6l106 -106q7 -8 6 -19t-8 -18l-158 -158h224q10 0 18.5 -7.5t10.5 -17.5q6 -41 6 -75q0 -15 -1.5 -34t-3.5 -30l-1 -11q-2 -10 -10.5 -17.5t-18.5 -7.5h-224l158 -158 q7 -7 8 -18t-6 -19l-106 -106q-8 -7 -19 -6t-18 8l-158 158v-224q0 -10 -7.5 -18.5t-17.5 -10.5q-41 -6 -75 -6q-15 0 -34 1.5t-30 3.5l-11 1q-10 2 -17.5 10.5t-7.5 18.5v224l-158 -158q-7 -7 -18 -8t-19 6l-106 106q-7 8 -6 19t8 18l158 158h-224q-10 0 -18.5 7.5 t-10.5 17.5q-6 41 -6 75q0 15 1.5 34t3.5 30l1 11q2 10 10.5 17.5t18.5 7.5h224l-158 158q-7 7 -8 18t6 19l106 106q8 7 19 6t18 -8l158 -158v224q0 10 7.5 18.5t17.5 10.5q41 6 75 6z" />
<glyph unicode="+" d="M450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-350h350q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-350v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v350h-350q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5 h350v350q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xa0;" />
<glyph unicode="&#xa5;" d="M825 1100h250q10 0 12.5 -5t-5.5 -13l-364 -364q-6 -6 -11 -18h268q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-100h275q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-174q0 -11 -7.5 -18.5t-18.5 -7.5h-148q-11 0 -18.5 7.5t-7.5 18.5v174 h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h125v100h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h118q-5 12 -11 18l-364 364q-8 8 -5.5 13t12.5 5h250q25 0 43 -18l164 -164q8 -8 18 -8t18 8l164 164q18 18 43 18z" />
<glyph unicode="&#x2000;" horiz-adv-x="650" />
<glyph unicode="&#x2001;" horiz-adv-x="1300" />
<glyph unicode="&#x2002;" horiz-adv-x="650" />
<glyph unicode="&#x2003;" horiz-adv-x="1300" />
<glyph unicode="&#x2004;" horiz-adv-x="433" />
<glyph unicode="&#x2005;" horiz-adv-x="325" />
<glyph unicode="&#x2006;" horiz-adv-x="216" />
<glyph unicode="&#x2007;" horiz-adv-x="216" />
<glyph unicode="&#x2008;" horiz-adv-x="162" />
<glyph unicode="&#x2009;" horiz-adv-x="260" />
<glyph unicode="&#x200a;" horiz-adv-x="72" />
<glyph unicode="&#x202f;" horiz-adv-x="260" />
<glyph unicode="&#x205f;" horiz-adv-x="325" />
<glyph unicode="&#x20ac;" d="M744 1198q242 0 354 -189q60 -104 66 -209h-181q0 45 -17.5 82.5t-43.5 61.5t-58 40.5t-60.5 24t-51.5 7.5q-19 0 -40.5 -5.5t-49.5 -20.5t-53 -38t-49 -62.5t-39 -89.5h379l-100 -100h-300q-6 -50 -6 -100h406l-100 -100h-300q9 -74 33 -132t52.5 -91t61.5 -54.5t59 -29 t47 -7.5q22 0 50.5 7.5t60.5 24.5t58 41t43.5 61t17.5 80h174q-30 -171 -128 -278q-107 -117 -274 -117q-206 0 -324 158q-36 48 -69 133t-45 204h-217l100 100h112q1 47 6 100h-218l100 100h134q20 87 51 153.5t62 103.5q117 141 297 141z" />
<glyph unicode="&#x20bd;" d="M428 1200h350q67 0 120 -13t86 -31t57 -49.5t35 -56.5t17 -64.5t6.5 -60.5t0.5 -57v-16.5v-16.5q0 -36 -0.5 -57t-6.5 -61t-17 -65t-35 -57t-57 -50.5t-86 -31.5t-120 -13h-178l-2 -100h288q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-138v-175q0 -11 -5.5 -18 t-15.5 -7h-149q-10 0 -17.5 7.5t-7.5 17.5v175h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v100h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v475q0 10 7.5 17.5t17.5 7.5zM600 1000v-300h203q64 0 86.5 33t22.5 119q0 84 -22.5 116t-86.5 32h-203z" />
<glyph unicode="&#x2212;" d="M250 700h800q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#x231b;" d="M1000 1200v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-50v-100q0 -91 -49.5 -165.5t-130.5 -109.5q81 -35 130.5 -109.5t49.5 -165.5v-150h50q21 0 35.5 -14.5t14.5 -35.5v-150h-800v150q0 21 14.5 35.5t35.5 14.5h50v150q0 91 49.5 165.5t130.5 109.5q-81 35 -130.5 109.5 t-49.5 165.5v100h-50q-21 0 -35.5 14.5t-14.5 35.5v150h800zM400 1000v-100q0 -60 32.5 -109.5t87.5 -73.5q28 -12 44 -37t16 -55t-16 -55t-44 -37q-55 -24 -87.5 -73.5t-32.5 -109.5v-150h400v150q0 60 -32.5 109.5t-87.5 73.5q-28 12 -44 37t-16 55t16 55t44 37 q55 24 87.5 73.5t32.5 109.5v100h-400z" />
<glyph unicode="&#x25fc;" horiz-adv-x="500" d="M0 0z" />
<glyph unicode="&#x2601;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -206.5q0 -121 -85 -207.5t-205 -86.5h-750q-79 0 -135.5 57t-56.5 137q0 69 42.5 122.5t108.5 67.5q-2 12 -2 37q0 153 108 260.5t260 107.5z" />
<glyph unicode="&#x26fa;" d="M774 1193.5q16 -9.5 20.5 -27t-5.5 -33.5l-136 -187l467 -746h30q20 0 35 -18.5t15 -39.5v-42h-1200v42q0 21 15 39.5t35 18.5h30l468 746l-135 183q-10 16 -5.5 34t20.5 28t34 5.5t28 -20.5l111 -148l112 150q9 16 27 20.5t34 -5zM600 200h377l-182 112l-195 534v-646z " />
<glyph unicode="&#x2709;" d="M25 1100h1150q10 0 12.5 -5t-5.5 -13l-564 -567q-8 -8 -18 -8t-18 8l-564 567q-8 8 -5.5 13t12.5 5zM18 882l264 -264q8 -8 8 -18t-8 -18l-264 -264q-8 -8 -13 -5.5t-5 12.5v550q0 10 5 12.5t13 -5.5zM918 618l264 264q8 8 13 5.5t5 -12.5v-550q0 -10 -5 -12.5t-13 5.5 l-264 264q-8 8 -8 18t8 18zM818 482l364 -364q8 -8 5.5 -13t-12.5 -5h-1150q-10 0 -12.5 5t5.5 13l364 364q8 8 18 8t18 -8l164 -164q8 -8 18 -8t18 8l164 164q8 8 18 8t18 -8z" />
<glyph unicode="&#x270f;" d="M1011 1210q19 0 33 -13l153 -153q13 -14 13 -33t-13 -33l-99 -92l-214 214l95 96q13 14 32 14zM1013 800l-615 -614l-214 214l614 614zM317 96l-333 -112l110 335z" />
<glyph unicode="&#xe001;" d="M700 650v-550h250q21 0 35.5 -14.5t14.5 -35.5v-50h-800v50q0 21 14.5 35.5t35.5 14.5h250v550l-500 550h1200z" />
<glyph unicode="&#xe002;" d="M368 1017l645 163q39 15 63 0t24 -49v-831q0 -55 -41.5 -95.5t-111.5 -63.5q-79 -25 -147 -4.5t-86 75t25.5 111.5t122.5 82q72 24 138 8v521l-600 -155v-606q0 -42 -44 -90t-109 -69q-79 -26 -147 -5.5t-86 75.5t25.5 111.5t122.5 82.5q72 24 138 7v639q0 38 14.5 59 t53.5 34z" />
<glyph unicode="&#xe003;" d="M500 1191q100 0 191 -39t156.5 -104.5t104.5 -156.5t39 -191l-1 -2l1 -5q0 -141 -78 -262l275 -274q23 -26 22.5 -44.5t-22.5 -42.5l-59 -58q-26 -20 -46.5 -20t-39.5 20l-275 274q-119 -77 -261 -77l-5 1l-2 -1q-100 0 -191 39t-156.5 104.5t-104.5 156.5t-39 191 t39 191t104.5 156.5t156.5 104.5t191 39zM500 1022q-88 0 -162 -43t-117 -117t-43 -162t43 -162t117 -117t162 -43t162 43t117 117t43 162t-43 162t-117 117t-162 43z" />
<glyph unicode="&#xe005;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104z" />
<glyph unicode="&#xe006;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429z" />
<glyph unicode="&#xe007;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429zM477 700h-240l197 -142l-74 -226 l193 139l195 -140l-74 229l192 140h-234l-78 211z" />
<glyph unicode="&#xe008;" d="M600 1200q124 0 212 -88t88 -212v-250q0 -46 -31 -98t-69 -52v-75q0 -10 6 -21.5t15 -17.5l358 -230q9 -5 15 -16.5t6 -21.5v-93q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v93q0 10 6 21.5t15 16.5l358 230q9 6 15 17.5t6 21.5v75q-38 0 -69 52 t-31 98v250q0 124 88 212t212 88z" />
<glyph unicode="&#xe009;" d="M25 1100h1150q10 0 17.5 -7.5t7.5 -17.5v-1050q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v1050q0 10 7.5 17.5t17.5 7.5zM100 1000v-100h100v100h-100zM875 1000h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5t17.5 -7.5h550 q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM1000 1000v-100h100v100h-100zM100 800v-100h100v100h-100zM1000 800v-100h100v100h-100zM100 600v-100h100v100h-100zM1000 600v-100h100v100h-100zM875 500h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5 t17.5 -7.5h550q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM100 400v-100h100v100h-100zM1000 400v-100h100v100h-100zM100 200v-100h100v100h-100zM1000 200v-100h100v100h-100z" />
<glyph unicode="&#xe010;" d="M50 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM50 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe011;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM850 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 700h200q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h200 q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5 t35.5 14.5z" />
<glyph unicode="&#xe012;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h700q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe013;" d="M465 477l571 571q8 8 18 8t17 -8l177 -177q8 -7 8 -17t-8 -18l-783 -784q-7 -8 -17.5 -8t-17.5 8l-384 384q-8 8 -8 18t8 17l177 177q7 8 17 8t18 -8l171 -171q7 -7 18 -7t18 7z" />
<glyph unicode="&#xe014;" d="M904 1083l178 -179q8 -8 8 -18.5t-8 -17.5l-267 -268l267 -268q8 -7 8 -17.5t-8 -18.5l-178 -178q-8 -8 -18.5 -8t-17.5 8l-268 267l-268 -267q-7 -8 -17.5 -8t-18.5 8l-178 178q-8 8 -8 18.5t8 17.5l267 268l-267 268q-8 7 -8 17.5t8 18.5l178 178q8 8 18.5 8t17.5 -8 l268 -267l268 268q7 7 17.5 7t18.5 -7z" />
<glyph unicode="&#xe015;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM425 900h150q10 0 17.5 -7.5t7.5 -17.5v-75h75q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5 t-17.5 -7.5h-75v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-75q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v75q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe016;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM325 800h350q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-350q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe017;" d="M550 1200h100q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM800 975v166q167 -62 272 -209.5t105 -331.5q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5 t-184.5 123t-123 184.5t-45.5 224q0 184 105 331.5t272 209.5v-166q-103 -55 -165 -155t-62 -220q0 -116 57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5q0 120 -62 220t-165 155z" />
<glyph unicode="&#xe018;" d="M1025 1200h150q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM725 800h150q10 0 17.5 -7.5t7.5 -17.5v-750q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v750 q0 10 7.5 17.5t17.5 7.5zM425 500h150q10 0 17.5 -7.5t7.5 -17.5v-450q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v450q0 10 7.5 17.5t17.5 7.5zM125 300h150q10 0 17.5 -7.5t7.5 -17.5v-250q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5 v250q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe019;" d="M600 1174q33 0 74 -5l38 -152l5 -1q49 -14 94 -39l5 -2l134 80q61 -48 104 -105l-80 -134l3 -5q25 -44 39 -93l1 -6l152 -38q5 -43 5 -73q0 -34 -5 -74l-152 -38l-1 -6q-15 -49 -39 -93l-3 -5l80 -134q-48 -61 -104 -105l-134 81l-5 -3q-44 -25 -94 -39l-5 -2l-38 -151 q-43 -5 -74 -5q-33 0 -74 5l-38 151l-5 2q-49 14 -94 39l-5 3l-134 -81q-60 48 -104 105l80 134l-3 5q-25 45 -38 93l-2 6l-151 38q-6 42 -6 74q0 33 6 73l151 38l2 6q13 48 38 93l3 5l-80 134q47 61 105 105l133 -80l5 2q45 25 94 39l5 1l38 152q43 5 74 5zM600 815 q-89 0 -152 -63t-63 -151.5t63 -151.5t152 -63t152 63t63 151.5t-63 151.5t-152 63z" />
<glyph unicode="&#xe020;" d="M500 1300h300q41 0 70.5 -29.5t29.5 -70.5v-100h275q10 0 17.5 -7.5t7.5 -17.5v-75h-1100v75q0 10 7.5 17.5t17.5 7.5h275v100q0 41 29.5 70.5t70.5 29.5zM500 1200v-100h300v100h-300zM1100 900v-800q0 -41 -29.5 -70.5t-70.5 -29.5h-700q-41 0 -70.5 29.5t-29.5 70.5 v800h900zM300 800v-700h100v700h-100zM500 800v-700h100v700h-100zM700 800v-700h100v700h-100zM900 800v-700h100v700h-100z" />
<glyph unicode="&#xe021;" d="M18 618l620 608q8 7 18.5 7t17.5 -7l608 -608q8 -8 5.5 -13t-12.5 -5h-175v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v375h-300v-375q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v575h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe022;" d="M600 1200v-400q0 -41 29.5 -70.5t70.5 -29.5h300v-650q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5h450zM1000 800h-250q-21 0 -35.5 14.5t-14.5 35.5v250z" />
<glyph unicode="&#xe023;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h50q10 0 17.5 -7.5t7.5 -17.5v-275h175q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe024;" d="M1300 0h-538l-41 400h-242l-41 -400h-538l431 1200h209l-21 -300h162l-20 300h208zM515 800l-27 -300h224l-27 300h-170z" />
<glyph unicode="&#xe025;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-450h191q20 0 25.5 -11.5t-7.5 -27.5l-327 -400q-13 -16 -32 -16t-32 16l-327 400q-13 16 -7.5 27.5t25.5 11.5h191v450q0 21 14.5 35.5t35.5 14.5zM1125 400h50q10 0 17.5 -7.5t7.5 -17.5v-350q0 -10 -7.5 -17.5t-17.5 -7.5 h-1050q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h50q10 0 17.5 -7.5t7.5 -17.5v-175h900v175q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe026;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -275q-13 -16 -32 -16t-32 16l-223 275q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z " />
<glyph unicode="&#xe027;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM632 914l223 -275q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5l223 275q13 16 32 16 t32 -16z" />
<glyph unicode="&#xe028;" d="M225 1200h750q10 0 19.5 -7t12.5 -17l186 -652q7 -24 7 -49v-425q0 -12 -4 -27t-9 -17q-12 -6 -37 -6h-1100q-12 0 -27 4t-17 8q-6 13 -6 38l1 425q0 25 7 49l185 652q3 10 12.5 17t19.5 7zM878 1000h-556q-10 0 -19 -7t-11 -18l-87 -450q-2 -11 4 -18t16 -7h150 q10 0 19.5 -7t11.5 -17l38 -152q2 -10 11.5 -17t19.5 -7h250q10 0 19.5 7t11.5 17l38 152q2 10 11.5 17t19.5 7h150q10 0 16 7t4 18l-87 450q-2 11 -11 18t-19 7z" />
<glyph unicode="&#xe029;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM540 820l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe030;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-362q0 -10 -7.5 -17.5t-17.5 -7.5h-362q-11 0 -13 5.5t5 12.5l133 133q-109 76 -238 76q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5h150q0 -117 -45.5 -224 t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117z" />
<glyph unicode="&#xe031;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-361q0 -11 -7.5 -18.5t-18.5 -7.5h-361q-11 0 -13 5.5t5 12.5l134 134q-110 75 -239 75q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5h-150q0 117 45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117zM1027 600h150 q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5q-192 0 -348 118l-134 -134q-7 -8 -12.5 -5.5t-5.5 12.5v360q0 11 7.5 18.5t18.5 7.5h360q10 0 12.5 -5.5t-5.5 -12.5l-133 -133q110 -76 240 -76q116 0 214.5 57t155.5 155.5t57 214.5z" />
<glyph unicode="&#xe032;" d="M125 1200h1050q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-1050q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM1075 1000h-850q-10 0 -17.5 -7.5t-7.5 -17.5v-850q0 -10 7.5 -17.5t17.5 -7.5h850q10 0 17.5 7.5t7.5 17.5v850 q0 10 -7.5 17.5t-17.5 7.5zM325 900h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 900h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 700h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 700h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 500h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 500h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 300h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 300h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe033;" d="M900 800v200q0 83 -58.5 141.5t-141.5 58.5h-300q-82 0 -141 -59t-59 -141v-200h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h900q41 0 70.5 29.5t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5h-100zM400 800v150q0 21 15 35.5t35 14.5h200 q20 0 35 -14.5t15 -35.5v-150h-300z" />
<glyph unicode="&#xe034;" d="M125 1100h50q10 0 17.5 -7.5t7.5 -17.5v-1075h-100v1075q0 10 7.5 17.5t17.5 7.5zM1075 1052q4 0 9 -2q16 -6 16 -23v-421q0 -6 -3 -12q-33 -59 -66.5 -99t-65.5 -58t-56.5 -24.5t-52.5 -6.5q-26 0 -57.5 6.5t-52.5 13.5t-60 21q-41 15 -63 22.5t-57.5 15t-65.5 7.5 q-85 0 -160 -57q-7 -5 -15 -5q-6 0 -11 3q-14 7 -14 22v438q22 55 82 98.5t119 46.5q23 2 43 0.5t43 -7t32.5 -8.5t38 -13t32.5 -11q41 -14 63.5 -21t57 -14t63.5 -7q103 0 183 87q7 8 18 8z" />
<glyph unicode="&#xe035;" d="M600 1175q116 0 227 -49.5t192.5 -131t131 -192.5t49.5 -227v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v300q0 127 -70.5 231.5t-184.5 161.5t-245 57t-245 -57t-184.5 -161.5t-70.5 -231.5v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50 q-10 0 -17.5 7.5t-7.5 17.5v300q0 116 49.5 227t131 192.5t192.5 131t227 49.5zM220 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460q0 8 6 14t14 6zM820 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460 q0 8 6 14t14 6z" />
<glyph unicode="&#xe036;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM900 668l120 120q7 7 17 7t17 -7l34 -34q7 -7 7 -17t-7 -17l-120 -120l120 -120q7 -7 7 -17 t-7 -17l-34 -34q-7 -7 -17 -7t-17 7l-120 119l-120 -119q-7 -7 -17 -7t-17 7l-34 34q-7 7 -7 17t7 17l119 120l-119 120q-7 7 -7 17t7 17l34 34q7 8 17 8t17 -8z" />
<glyph unicode="&#xe037;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6 l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238q-6 8 -4.5 18t9.5 17l29 22q7 5 15 5z" />
<glyph unicode="&#xe038;" d="M967 1004h3q11 -1 17 -10q135 -179 135 -396q0 -105 -34 -206.5t-98 -185.5q-7 -9 -17 -10h-3q-9 0 -16 6l-42 34q-8 6 -9 16t5 18q111 150 111 328q0 90 -29.5 176t-84.5 157q-6 9 -5 19t10 16l42 33q7 5 15 5zM321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5 t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238 q-6 8 -4.5 18.5t9.5 16.5l29 22q7 5 15 5z" />
<glyph unicode="&#xe039;" d="M500 900h100v-100h-100v-100h-400v-100h-100v600h500v-300zM1200 700h-200v-100h200v-200h-300v300h-200v300h-100v200h600v-500zM100 1100v-300h300v300h-300zM800 1100v-300h300v300h-300zM300 900h-100v100h100v-100zM1000 900h-100v100h100v-100zM300 500h200v-500 h-500v500h200v100h100v-100zM800 300h200v-100h-100v-100h-200v100h-100v100h100v200h-200v100h300v-300zM100 400v-300h300v300h-300zM300 200h-100v100h100v-100zM1200 200h-100v100h100v-100zM700 0h-100v100h100v-100zM1200 0h-300v100h300v-100z" />
<glyph unicode="&#xe040;" d="M100 200h-100v1000h100v-1000zM300 200h-100v1000h100v-1000zM700 200h-200v1000h200v-1000zM900 200h-100v1000h100v-1000zM1200 200h-200v1000h200v-1000zM400 0h-300v100h300v-100zM600 0h-100v91h100v-91zM800 0h-100v91h100v-91zM1100 0h-200v91h200v-91z" />
<glyph unicode="&#xe041;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe042;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM800 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-56 56l424 426l-700 700h150zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5 t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe043;" d="M300 1200h825q75 0 75 -75v-900q0 -25 -18 -43l-64 -64q-8 -8 -13 -5.5t-5 12.5v950q0 10 -7.5 17.5t-17.5 7.5h-700q-25 0 -43 -18l-64 -64q-8 -8 -5.5 -13t12.5 -5h700q10 0 17.5 -7.5t7.5 -17.5v-950q0 -10 -7.5 -17.5t-17.5 -7.5h-850q-10 0 -17.5 7.5t-7.5 17.5v975 q0 25 18 43l139 139q18 18 43 18z" />
<glyph unicode="&#xe044;" d="M250 1200h800q21 0 35.5 -14.5t14.5 -35.5v-1150l-450 444l-450 -445v1151q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe045;" d="M822 1200h-444q-11 0 -19 -7.5t-9 -17.5l-78 -301q-7 -24 7 -45l57 -108q6 -9 17.5 -15t21.5 -6h450q10 0 21.5 6t17.5 15l62 108q14 21 7 45l-83 301q-1 10 -9 17.5t-19 7.5zM1175 800h-150q-10 0 -21 -6.5t-15 -15.5l-78 -156q-4 -9 -15 -15.5t-21 -6.5h-550 q-10 0 -21 6.5t-15 15.5l-78 156q-4 9 -15 15.5t-21 6.5h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-650q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h750q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5 t7.5 17.5v650q0 10 -7.5 17.5t-17.5 7.5zM850 200h-500q-10 0 -19.5 -7t-11.5 -17l-38 -152q-2 -10 3.5 -17t15.5 -7h600q10 0 15.5 7t3.5 17l-38 152q-2 10 -11.5 17t-19.5 7z" />
<glyph unicode="&#xe046;" d="M500 1100h200q56 0 102.5 -20.5t72.5 -50t44 -59t25 -50.5l6 -20h150q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5h150q2 8 6.5 21.5t24 48t45 61t72 48t102.5 21.5zM900 800v-100 h100v100h-100zM600 730q-95 0 -162.5 -67.5t-67.5 -162.5t67.5 -162.5t162.5 -67.5t162.5 67.5t67.5 162.5t-67.5 162.5t-162.5 67.5zM600 603q43 0 73 -30t30 -73t-30 -73t-73 -30t-73 30t-30 73t30 73t73 30z" />
<glyph unicode="&#xe047;" d="M681 1199l385 -998q20 -50 60 -92q18 -19 36.5 -29.5t27.5 -11.5l10 -2v-66h-417v66q53 0 75 43.5t5 88.5l-82 222h-391q-58 -145 -92 -234q-11 -34 -6.5 -57t25.5 -37t46 -20t55 -6v-66h-365v66q56 24 84 52q12 12 25 30.5t20 31.5l7 13l399 1006h93zM416 521h340 l-162 457z" />
<glyph unicode="&#xe048;" d="M753 641q5 -1 14.5 -4.5t36 -15.5t50.5 -26.5t53.5 -40t50.5 -54.5t35.5 -70t14.5 -87q0 -67 -27.5 -125.5t-71.5 -97.5t-98.5 -66.5t-108.5 -40.5t-102 -13h-500v89q41 7 70.5 32.5t29.5 65.5v827q0 24 -0.5 34t-3.5 24t-8.5 19.5t-17 13.5t-28 12.5t-42.5 11.5v71 l471 -1q57 0 115.5 -20.5t108 -57t80.5 -94t31 -124.5q0 -51 -15.5 -96.5t-38 -74.5t-45 -50.5t-38.5 -30.5zM400 700h139q78 0 130.5 48.5t52.5 122.5q0 41 -8.5 70.5t-29.5 55.5t-62.5 39.5t-103.5 13.5h-118v-350zM400 200h216q80 0 121 50.5t41 130.5q0 90 -62.5 154.5 t-156.5 64.5h-159v-400z" />
<glyph unicode="&#xe049;" d="M877 1200l2 -57q-83 -19 -116 -45.5t-40 -66.5l-132 -839q-9 -49 13 -69t96 -26v-97h-500v97q186 16 200 98l173 832q3 17 3 30t-1.5 22.5t-9 17.5t-13.5 12.5t-21.5 10t-26 8.5t-33.5 10q-13 3 -19 5v57h425z" />
<glyph unicode="&#xe050;" d="M1300 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM175 1000h-75v-800h75l-125 -167l-125 167h75v800h-75l125 167z" />
<glyph unicode="&#xe051;" d="M1100 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-650q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v650h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM1167 50l-167 -125v75h-800v-75l-167 125l167 125v-75h800v75z" />
<glyph unicode="&#xe052;" d="M50 1100h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe053;" d="M250 1100h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM250 500h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe054;" d="M500 950v100q0 21 14.5 35.5t35.5 14.5h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5zM100 650v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000 q-21 0 -35.5 14.5t-14.5 35.5zM300 350v100q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5zM0 50v100q0 21 14.5 35.5t35.5 14.5h1100q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5z" />
<glyph unicode="&#xe055;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe056;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 1100h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 800h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 500h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 500h800q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 200h800 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe057;" d="M400 0h-100v1100h100v-1100zM550 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM267 550l-167 -125v75h-200v100h200v75zM550 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe058;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM900 0h-100v1100h100v-1100zM50 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM1100 600h200v-100h-200v-75l-167 125l167 125v-75zM50 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe059;" d="M75 1000h750q31 0 53 -22t22 -53v-650q0 -31 -22 -53t-53 -22h-750q-31 0 -53 22t-22 53v650q0 31 22 53t53 22zM1200 300l-300 300l300 300v-600z" />
<glyph unicode="&#xe060;" d="M44 1100h1112q18 0 31 -13t13 -31v-1012q0 -18 -13 -31t-31 -13h-1112q-18 0 -31 13t-13 31v1012q0 18 13 31t31 13zM100 1000v-737l247 182l298 -131l-74 156l293 318l236 -288v500h-1000zM342 884q56 0 95 -39t39 -94.5t-39 -95t-95 -39.5t-95 39.5t-39 95t39 94.5 t95 39z" />
<glyph unicode="&#xe062;" d="M648 1169q117 0 216 -60t156.5 -161t57.5 -218q0 -115 -70 -258q-69 -109 -158 -225.5t-143 -179.5l-54 -62q-9 8 -25.5 24.5t-63.5 67.5t-91 103t-98.5 128t-95.5 148q-60 132 -60 249q0 88 34 169.5t91.5 142t137 96.5t166.5 36zM652.5 974q-91.5 0 -156.5 -65 t-65 -157t65 -156.5t156.5 -64.5t156.5 64.5t65 156.5t-65 157t-156.5 65z" />
<glyph unicode="&#xe063;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 173v854q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57z" />
<glyph unicode="&#xe064;" d="M554 1295q21 -72 57.5 -143.5t76 -130t83 -118t82.5 -117t70 -116t49.5 -126t18.5 -136.5q0 -71 -25.5 -135t-68.5 -111t-99 -82t-118.5 -54t-125.5 -23q-84 5 -161.5 34t-139.5 78.5t-99 125t-37 164.5q0 69 18 136.5t49.5 126.5t69.5 116.5t81.5 117.5t83.5 119 t76.5 131t58.5 143zM344 710q-23 -33 -43.5 -70.5t-40.5 -102.5t-17 -123q1 -37 14.5 -69.5t30 -52t41 -37t38.5 -24.5t33 -15q21 -7 32 -1t13 22l6 34q2 10 -2.5 22t-13.5 19q-5 4 -14 12t-29.5 40.5t-32.5 73.5q-26 89 6 271q2 11 -6 11q-8 1 -15 -10z" />
<glyph unicode="&#xe065;" d="M1000 1013l108 115q2 1 5 2t13 2t20.5 -1t25 -9.5t28.5 -21.5q22 -22 27 -43t0 -32l-6 -10l-108 -115zM350 1100h400q50 0 105 -13l-187 -187h-368q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v182l200 200v-332 q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM1009 803l-362 -362l-161 -50l55 170l355 355z" />
<glyph unicode="&#xe066;" d="M350 1100h361q-164 -146 -216 -200h-195q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5l200 153v-103q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M824 1073l339 -301q8 -7 8 -17.5t-8 -17.5l-340 -306q-7 -6 -12.5 -4t-6.5 11v203q-26 1 -54.5 0t-78.5 -7.5t-92 -17.5t-86 -35t-70 -57q10 59 33 108t51.5 81.5t65 58.5t68.5 40.5t67 24.5t56 13.5t40 4.5v210q1 10 6.5 12.5t13.5 -4.5z" />
<glyph unicode="&#xe067;" d="M350 1100h350q60 0 127 -23l-178 -177h-349q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v69l200 200v-219q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M643 639l395 395q7 7 17.5 7t17.5 -7l101 -101q7 -7 7 -17.5t-7 -17.5l-531 -532q-7 -7 -17.5 -7t-17.5 7l-248 248q-7 7 -7 17.5t7 17.5l101 101q7 7 17.5 7t17.5 -7l111 -111q8 -7 18 -7t18 7z" />
<glyph unicode="&#xe068;" d="M318 918l264 264q8 8 18 8t18 -8l260 -264q7 -8 4.5 -13t-12.5 -5h-170v-200h200v173q0 10 5 12t13 -5l264 -260q8 -7 8 -17.5t-8 -17.5l-264 -265q-8 -7 -13 -5t-5 12v173h-200v-200h170q10 0 12.5 -5t-4.5 -13l-260 -264q-8 -8 -18 -8t-18 8l-264 264q-8 8 -5.5 13 t12.5 5h175v200h-200v-173q0 -10 -5 -12t-13 5l-264 265q-8 7 -8 17.5t8 17.5l264 260q8 7 13 5t5 -12v-173h200v200h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe069;" d="M250 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe070;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5 t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe071;" d="M1200 1050v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-492 480q-15 14 -15 35t15 35l492 480q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25z" />
<glyph unicode="&#xe072;" d="M243 1074l814 -498q18 -11 18 -26t-18 -26l-814 -498q-18 -11 -30.5 -4t-12.5 28v1000q0 21 12.5 28t30.5 -4z" />
<glyph unicode="&#xe073;" d="M250 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM650 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe074;" d="M1100 950v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe075;" d="M500 612v438q0 21 10.5 25t25.5 -10l492 -480q15 -14 15 -35t-15 -35l-492 -480q-15 -14 -25.5 -10t-10.5 25v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10z" />
<glyph unicode="&#xe076;" d="M1048 1102l100 1q20 0 35 -14.5t15 -35.5l5 -1000q0 -21 -14.5 -35.5t-35.5 -14.5l-100 -1q-21 0 -35.5 14.5t-14.5 35.5l-2 437l-463 -454q-14 -15 -24.5 -10.5t-10.5 25.5l-2 437l-462 -455q-15 -14 -25.5 -9.5t-10.5 24.5l-5 1000q0 21 10.5 25.5t25.5 -10.5l466 -450 l-2 438q0 20 10.5 24.5t25.5 -9.5l466 -451l-2 438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe077;" d="M850 1100h100q21 0 35.5 -14.5t14.5 -35.5v-1000q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10l464 -453v438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe078;" d="M686 1081l501 -540q15 -15 10.5 -26t-26.5 -11h-1042q-22 0 -26.5 11t10.5 26l501 540q15 15 36 15t36 -15zM150 400h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe079;" d="M885 900l-352 -353l352 -353l-197 -198l-552 552l552 550z" />
<glyph unicode="&#xe080;" d="M1064 547l-551 -551l-198 198l353 353l-353 353l198 198z" />
<glyph unicode="&#xe081;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM650 900h-100q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-150 q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5h150v-150q0 -21 14.5 -35.5t35.5 -14.5h100q21 0 35.5 14.5t14.5 35.5v150h150q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-150v150q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe082;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM850 700h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5 t35.5 -14.5h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe083;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM741.5 913q-12.5 0 -21.5 -9l-120 -120l-120 120q-9 9 -21.5 9 t-21.5 -9l-141 -141q-9 -9 -9 -21.5t9 -21.5l120 -120l-120 -120q-9 -9 -9 -21.5t9 -21.5l141 -141q9 -9 21.5 -9t21.5 9l120 120l120 -120q9 -9 21.5 -9t21.5 9l141 141q9 9 9 21.5t-9 21.5l-120 120l120 120q9 9 9 21.5t-9 21.5l-141 141q-9 9 -21.5 9z" />
<glyph unicode="&#xe084;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM546 623l-84 85q-7 7 -17.5 7t-18.5 -7l-139 -139q-7 -8 -7 -18t7 -18 l242 -241q7 -8 17.5 -8t17.5 8l375 375q7 7 7 17.5t-7 18.5l-139 139q-7 7 -17.5 7t-17.5 -7z" />
<glyph unicode="&#xe085;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM588 941q-29 0 -59 -5.5t-63 -20.5t-58 -38.5t-41.5 -63t-16.5 -89.5 q0 -25 20 -25h131q30 -5 35 11q6 20 20.5 28t45.5 8q20 0 31.5 -10.5t11.5 -28.5q0 -23 -7 -34t-26 -18q-1 0 -13.5 -4t-19.5 -7.5t-20 -10.5t-22 -17t-18.5 -24t-15.5 -35t-8 -46q-1 -8 5.5 -16.5t20.5 -8.5h173q7 0 22 8t35 28t37.5 48t29.5 74t12 100q0 47 -17 83 t-42.5 57t-59.5 34.5t-64 18t-59 4.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe086;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM675 1000h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5 t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5zM675 700h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h75v-200h-75q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h350q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5 t-17.5 7.5h-75v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe087;" d="M525 1200h150q10 0 17.5 -7.5t7.5 -17.5v-194q103 -27 178.5 -102.5t102.5 -178.5h194q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-194q-27 -103 -102.5 -178.5t-178.5 -102.5v-194q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v194 q-103 27 -178.5 102.5t-102.5 178.5h-194q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h194q27 103 102.5 178.5t178.5 102.5v194q0 10 7.5 17.5t17.5 7.5zM700 893v-168q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v168q-68 -23 -119 -74 t-74 -119h168q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-168q23 -68 74 -119t119 -74v168q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-168q68 23 119 74t74 119h-168q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h168 q-23 68 -74 119t-119 74z" />
<glyph unicode="&#xe088;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM759 823l64 -64q7 -7 7 -17.5t-7 -17.5l-124 -124l124 -124q7 -7 7 -17.5t-7 -17.5l-64 -64q-7 -7 -17.5 -7t-17.5 7l-124 124l-124 -124q-7 -7 -17.5 -7t-17.5 7l-64 64 q-7 7 -7 17.5t7 17.5l124 124l-124 124q-7 7 -7 17.5t7 17.5l64 64q7 7 17.5 7t17.5 -7l124 -124l124 124q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe089;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM782 788l106 -106q7 -7 7 -17.5t-7 -17.5l-320 -321q-8 -7 -18 -7t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l197 197q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe090;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5q0 -120 65 -225 l587 587q-105 65 -225 65zM965 819l-584 -584q104 -62 219 -62q116 0 214.5 57t155.5 155.5t57 214.5q0 115 -62 219z" />
<glyph unicode="&#xe091;" d="M39 582l522 427q16 13 27.5 8t11.5 -26v-291h550q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-550v-291q0 -21 -11.5 -26t-27.5 8l-522 427q-16 13 -16 32t16 32z" />
<glyph unicode="&#xe092;" d="M639 1009l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291h-550q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h550v291q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe093;" d="M682 1161l427 -522q13 -16 8 -27.5t-26 -11.5h-291v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v550h-291q-21 0 -26 11.5t8 27.5l427 522q13 16 32 16t32 -16z" />
<glyph unicode="&#xe094;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-550h291q21 0 26 -11.5t-8 -27.5l-427 -522q-13 -16 -32 -16t-32 16l-427 522q-13 16 -8 27.5t26 11.5h291v550q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe095;" d="M639 1109l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291q-94 -2 -182 -20t-170.5 -52t-147 -92.5t-100.5 -135.5q5 105 27 193.5t67.5 167t113 135t167 91.5t225.5 42v262q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe096;" d="M850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5zM350 0h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249 q8 7 18 7t18 -7l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5z" />
<glyph unicode="&#xe097;" d="M1014 1120l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249q8 7 18 7t18 -7zM250 600h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5z" />
<glyph unicode="&#xe101;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM704 900h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5 t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe102;" d="M260 1200q9 0 19 -2t15 -4l5 -2q22 -10 44 -23l196 -118q21 -13 36 -24q29 -21 37 -12q11 13 49 35l196 118q22 13 45 23q17 7 38 7q23 0 47 -16.5t37 -33.5l13 -16q14 -21 18 -45l25 -123l8 -44q1 -9 8.5 -14.5t17.5 -5.5h61q10 0 17.5 -7.5t7.5 -17.5v-50 q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 -7.5t-7.5 -17.5v-175h-400v300h-200v-300h-400v175q0 10 -7.5 17.5t-17.5 7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5h61q11 0 18 3t7 8q0 4 9 52l25 128q5 25 19 45q2 3 5 7t13.5 15t21.5 19.5t26.5 15.5 t29.5 7zM915 1079l-166 -162q-7 -7 -5 -12t12 -5h219q10 0 15 7t2 17l-51 149q-3 10 -11 12t-15 -6zM463 917l-177 157q-8 7 -16 5t-11 -12l-51 -143q-3 -10 2 -17t15 -7h231q11 0 12.5 5t-5.5 12zM500 0h-375q-10 0 -17.5 7.5t-7.5 17.5v375h400v-400zM1100 400v-375 q0 -10 -7.5 -17.5t-17.5 -7.5h-375v400h400z" />
<glyph unicode="&#xe103;" d="M1165 1190q8 3 21 -6.5t13 -17.5q-2 -178 -24.5 -323.5t-55.5 -245.5t-87 -174.5t-102.5 -118.5t-118 -68.5t-118.5 -33t-120 -4.5t-105 9.5t-90 16.5q-61 12 -78 11q-4 1 -12.5 0t-34 -14.5t-52.5 -40.5l-153 -153q-26 -24 -37 -14.5t-11 43.5q0 64 42 102q8 8 50.5 45 t66.5 58q19 17 35 47t13 61q-9 55 -10 102.5t7 111t37 130t78 129.5q39 51 80 88t89.5 63.5t94.5 45t113.5 36t129 31t157.5 37t182 47.5zM1116 1098q-8 9 -22.5 -3t-45.5 -50q-38 -47 -119 -103.5t-142 -89.5l-62 -33q-56 -30 -102 -57t-104 -68t-102.5 -80.5t-85.5 -91 t-64 -104.5q-24 -56 -31 -86t2 -32t31.5 17.5t55.5 59.5q25 30 94 75.5t125.5 77.5t147.5 81q70 37 118.5 69t102 79.5t99 111t86.5 148.5q22 50 24 60t-6 19z" />
<glyph unicode="&#xe104;" d="M653 1231q-39 -67 -54.5 -131t-10.5 -114.5t24.5 -96.5t47.5 -80t63.5 -62.5t68.5 -46.5t65 -30q-4 7 -17.5 35t-18.5 39.5t-17 39.5t-17 43t-13 42t-9.5 44.5t-2 42t4 43t13.5 39t23 38.5q96 -42 165 -107.5t105 -138t52 -156t13 -159t-19 -149.5q-13 -55 -44 -106.5 t-68 -87t-78.5 -64.5t-72.5 -45t-53 -22q-72 -22 -127 -11q-31 6 -13 19q6 3 17 7q13 5 32.5 21t41 44t38.5 63.5t21.5 81.5t-6.5 94.5t-50 107t-104 115.5q10 -104 -0.5 -189t-37 -140.5t-65 -93t-84 -52t-93.5 -11t-95 24.5q-80 36 -131.5 114t-53.5 171q-2 23 0 49.5 t4.5 52.5t13.5 56t27.5 60t46 64.5t69.5 68.5q-8 -53 -5 -102.5t17.5 -90t34 -68.5t44.5 -39t49 -2q31 13 38.5 36t-4.5 55t-29 64.5t-36 75t-26 75.5q-15 85 2 161.5t53.5 128.5t85.5 92.5t93.5 61t81.5 25.5z" />
<glyph unicode="&#xe105;" d="M600 1094q82 0 160.5 -22.5t140 -59t116.5 -82.5t94.5 -95t68 -95t42.5 -82.5t14 -57.5t-14 -57.5t-43 -82.5t-68.5 -95t-94.5 -95t-116.5 -82.5t-140 -59t-159.5 -22.5t-159.5 22.5t-140 59t-116.5 82.5t-94.5 95t-68.5 95t-43 82.5t-14 57.5t14 57.5t42.5 82.5t68 95 t94.5 95t116.5 82.5t140 59t160.5 22.5zM888 829q-15 15 -18 12t5 -22q25 -57 25 -119q0 -124 -88 -212t-212 -88t-212 88t-88 212q0 59 23 114q8 19 4.5 22t-17.5 -12q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q22 -36 47 -71t70 -82t92.5 -81t113 -58.5t133.5 -24.5 t133.5 24t113 58.5t92.5 81.5t70 81.5t47 70.5q11 18 9 42.5t-14 41.5q-90 117 -163 189zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l35 34q14 15 12.5 33.5t-16.5 33.5q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe106;" d="M592 0h-148l31 120q-91 20 -175.5 68.5t-143.5 106.5t-103.5 119t-66.5 110t-22 76q0 21 14 57.5t42.5 82.5t68 95t94.5 95t116.5 82.5t140 59t160.5 22.5q61 0 126 -15l32 121h148zM944 770l47 181q108 -85 176.5 -192t68.5 -159q0 -26 -19.5 -71t-59.5 -102t-93 -112 t-129 -104.5t-158 -75.5l46 173q77 49 136 117t97 131q11 18 9 42.5t-14 41.5q-54 70 -107 130zM310 824q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q18 -30 39 -60t57 -70.5t74 -73t90 -61t105 -41.5l41 154q-107 18 -178.5 101.5t-71.5 193.5q0 59 23 114q8 19 4.5 22 t-17.5 -12zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l12 11l22 86l-3 4q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe107;" d="M-90 100l642 1066q20 31 48 28.5t48 -35.5l642 -1056q21 -32 7.5 -67.5t-50.5 -35.5h-1294q-37 0 -50.5 34t7.5 66zM155 200h345v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h345l-445 723zM496 700h208q20 0 32 -14.5t8 -34.5l-58 -252 q-4 -20 -21.5 -34.5t-37.5 -14.5h-54q-20 0 -37.5 14.5t-21.5 34.5l-58 252q-4 20 8 34.5t32 14.5z" />
<glyph unicode="&#xe108;" d="M650 1200q62 0 106 -44t44 -106v-339l363 -325q15 -14 26 -38.5t11 -44.5v-41q0 -20 -12 -26.5t-29 5.5l-359 249v-263q100 -93 100 -113v-64q0 -21 -13 -29t-32 1l-205 128l-205 -128q-19 -9 -32 -1t-13 29v64q0 20 100 113v263l-359 -249q-17 -12 -29 -5.5t-12 26.5v41 q0 20 11 44.5t26 38.5l363 325v339q0 62 44 106t106 44z" />
<glyph unicode="&#xe109;" d="M850 1200h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-150h-1100v150q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5h100q21 0 35.5 -14.5t14.5 -35.5v-50h500v50q0 21 14.5 35.5t35.5 14.5zM1100 800v-750q0 -21 -14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v750h1100zM100 600v-100h100v100h-100zM300 600v-100h100v100h-100zM500 600v-100h100v100h-100zM700 600v-100h100v100h-100zM900 600v-100h100v100h-100zM100 400v-100h100v100h-100zM300 400v-100h100v100h-100zM500 400 v-100h100v100h-100zM700 400v-100h100v100h-100zM900 400v-100h100v100h-100zM100 200v-100h100v100h-100zM300 200v-100h100v100h-100zM500 200v-100h100v100h-100zM700 200v-100h100v100h-100zM900 200v-100h100v100h-100z" />
<glyph unicode="&#xe110;" d="M1135 1165l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-159l-600 -600h-291q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h209l600 600h241v150q0 21 10.5 25t24.5 -10zM522 819l-141 -141l-122 122h-209q-21 0 -35.5 14.5 t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h291zM1135 565l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-241l-181 181l141 141l122 -122h159v150q0 21 10.5 25t24.5 -10z" />
<glyph unicode="&#xe111;" d="M100 1100h1000q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-596l-304 -300v300h-100q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5z" />
<glyph unicode="&#xe112;" d="M150 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM850 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM1100 800v-300q0 -41 -3 -77.5t-15 -89.5t-32 -96t-58 -89t-89 -77t-129 -51t-174 -20t-174 20 t-129 51t-89 77t-58 89t-32 96t-15 89.5t-3 77.5v300h300v-250v-27v-42.5t1.5 -41t5 -38t10 -35t16.5 -30t25.5 -24.5t35 -19t46.5 -12t60 -4t60 4.5t46.5 12.5t35 19.5t25 25.5t17 30.5t10 35t5 38t2 40.5t-0.5 42v25v250h300z" />
<glyph unicode="&#xe113;" d="M1100 411l-198 -199l-353 353l-353 -353l-197 199l551 551z" />
<glyph unicode="&#xe114;" d="M1101 789l-550 -551l-551 551l198 199l353 -353l353 353z" />
<glyph unicode="&#xe115;" d="M404 1000h746q21 0 35.5 -14.5t14.5 -35.5v-551h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v401h-381zM135 984l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-400h385l215 -200h-750q-21 0 -35.5 14.5 t-14.5 35.5v550h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe116;" d="M56 1200h94q17 0 31 -11t18 -27l38 -162h896q24 0 39 -18.5t10 -42.5l-100 -475q-5 -21 -27 -42.5t-55 -21.5h-633l48 -200h535q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-50q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-300v-50 q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-31q-18 0 -32.5 10t-20.5 19l-5 10l-201 961h-54q-20 0 -35 14.5t-15 35.5t15 35.5t35 14.5z" />
<glyph unicode="&#xe117;" d="M1200 1000v-100h-1200v100h200q0 41 29.5 70.5t70.5 29.5h300q41 0 70.5 -29.5t29.5 -70.5h500zM0 800h1200v-800h-1200v800z" />
<glyph unicode="&#xe118;" d="M200 800l-200 -400v600h200q0 41 29.5 70.5t70.5 29.5h300q42 0 71 -29.5t29 -70.5h500v-200h-1000zM1500 700l-300 -700h-1200l300 700h1200z" />
<glyph unicode="&#xe119;" d="M635 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-601h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v601h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe120;" d="M936 864l249 -229q14 -15 14 -35.5t-14 -35.5l-249 -229q-15 -15 -25.5 -10.5t-10.5 24.5v151h-600v-151q0 -20 -10.5 -24.5t-25.5 10.5l-249 229q-14 15 -14 35.5t14 35.5l249 229q15 15 25.5 10.5t10.5 -25.5v-149h600v149q0 21 10.5 25.5t25.5 -10.5z" />
<glyph unicode="&#xe121;" d="M1169 400l-172 732q-5 23 -23 45.5t-38 22.5h-672q-20 0 -38 -20t-23 -41l-172 -739h1138zM1100 300h-1000q-41 0 -70.5 -29.5t-29.5 -70.5v-100q0 -41 29.5 -70.5t70.5 -29.5h1000q41 0 70.5 29.5t29.5 70.5v100q0 41 -29.5 70.5t-70.5 29.5zM800 100v100h100v-100h-100 zM1000 100v100h100v-100h-100z" />
<glyph unicode="&#xe122;" d="M1150 1100q21 0 35.5 -14.5t14.5 -35.5v-850q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v850q0 21 14.5 35.5t35.5 14.5zM1000 200l-675 200h-38l47 -276q3 -16 -5.5 -20t-29.5 -4h-7h-84q-20 0 -34.5 14t-18.5 35q-55 337 -55 351v250v6q0 16 1 23.5t6.5 14 t17.5 6.5h200l675 250v-850zM0 750v-250q-4 0 -11 0.5t-24 6t-30 15t-24 30t-11 48.5v50q0 26 10.5 46t25 30t29 16t25.5 7z" />
<glyph unicode="&#xe123;" d="M553 1200h94q20 0 29 -10.5t3 -29.5l-18 -37q83 -19 144 -82.5t76 -140.5l63 -327l118 -173h17q19 0 33 -14.5t14 -35t-13 -40.5t-31 -27q-8 -4 -23 -9.5t-65 -19.5t-103 -25t-132.5 -20t-158.5 -9q-57 0 -115 5t-104 12t-88.5 15.5t-73.5 17.5t-54.5 16t-35.5 12l-11 4 q-18 8 -31 28t-13 40.5t14 35t33 14.5h17l118 173l63 327q15 77 76 140t144 83l-18 32q-6 19 3.5 32t28.5 13zM498 110q50 -6 102 -6q53 0 102 6q-12 -49 -39.5 -79.5t-62.5 -30.5t-63 30.5t-39 79.5z" />
<glyph unicode="&#xe124;" d="M800 946l224 78l-78 -224l234 -45l-180 -155l180 -155l-234 -45l78 -224l-224 78l-45 -234l-155 180l-155 -180l-45 234l-224 -78l78 224l-234 45l180 155l-180 155l234 45l-78 224l224 -78l45 234l155 -180l155 180z" />
<glyph unicode="&#xe125;" d="M650 1200h50q40 0 70 -40.5t30 -84.5v-150l-28 -125h328q40 0 70 -40.5t30 -84.5v-100q0 -45 -29 -74l-238 -344q-16 -24 -38 -40.5t-45 -16.5h-250q-7 0 -42 25t-66 50l-31 25h-61q-45 0 -72.5 18t-27.5 57v400q0 36 20 63l145 196l96 198q13 28 37.5 48t51.5 20z M650 1100l-100 -212l-150 -213v-375h100l136 -100h214l250 375v125h-450l50 225v175h-50zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe126;" d="M600 1100h250q23 0 45 -16.5t38 -40.5l238 -344q29 -29 29 -74v-100q0 -44 -30 -84.5t-70 -40.5h-328q28 -118 28 -125v-150q0 -44 -30 -84.5t-70 -40.5h-50q-27 0 -51.5 20t-37.5 48l-96 198l-145 196q-20 27 -20 63v400q0 39 27.5 57t72.5 18h61q124 100 139 100z M50 1000h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM636 1000l-136 -100h-100v-375l150 -213l100 -212h50v175l-50 225h450v125l-250 375h-214z" />
<glyph unicode="&#xe127;" d="M356 873l363 230q31 16 53 -6l110 -112q13 -13 13.5 -32t-11.5 -34l-84 -121h302q84 0 138 -38t54 -110t-55 -111t-139 -39h-106l-131 -339q-6 -21 -19.5 -41t-28.5 -20h-342q-7 0 -90 81t-83 94v525q0 17 14 35.5t28 28.5zM400 792v-503l100 -89h293l131 339 q6 21 19.5 41t28.5 20h203q21 0 30.5 25t0.5 50t-31 25h-456h-7h-6h-5.5t-6 0.5t-5 1.5t-5 2t-4 2.5t-4 4t-2.5 4.5q-12 25 5 47l146 183l-86 83zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe128;" d="M475 1103l366 -230q2 -1 6 -3.5t14 -10.5t18 -16.5t14.5 -20t6.5 -22.5v-525q0 -13 -86 -94t-93 -81h-342q-15 0 -28.5 20t-19.5 41l-131 339h-106q-85 0 -139.5 39t-54.5 111t54 110t138 38h302l-85 121q-11 15 -10.5 34t13.5 32l110 112q22 22 53 6zM370 945l146 -183 q17 -22 5 -47q-2 -2 -3.5 -4.5t-4 -4t-4 -2.5t-5 -2t-5 -1.5t-6 -0.5h-6h-6.5h-6h-475v-100h221q15 0 29 -20t20 -41l130 -339h294l106 89v503l-342 236zM1050 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5 v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe129;" d="M550 1294q72 0 111 -55t39 -139v-106l339 -131q21 -6 41 -19.5t20 -28.5v-342q0 -7 -81 -90t-94 -83h-525q-17 0 -35.5 14t-28.5 28l-9 14l-230 363q-16 31 6 53l112 110q13 13 32 13.5t34 -11.5l121 -84v302q0 84 38 138t110 54zM600 972v203q0 21 -25 30.5t-50 0.5 t-25 -31v-456v-7v-6v-5.5t-0.5 -6t-1.5 -5t-2 -5t-2.5 -4t-4 -4t-4.5 -2.5q-25 -12 -47 5l-183 146l-83 -86l236 -339h503l89 100v293l-339 131q-21 6 -41 19.5t-20 28.5zM450 200h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe130;" d="M350 1100h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5zM600 306v-106q0 -84 -39 -139t-111 -55t-110 54t-38 138v302l-121 -84q-15 -12 -34 -11.5t-32 13.5l-112 110 q-22 22 -6 53l230 363q1 2 3.5 6t10.5 13.5t16.5 17t20 13.5t22.5 6h525q13 0 94 -83t81 -90v-342q0 -15 -20 -28.5t-41 -19.5zM308 900l-236 -339l83 -86l183 146q22 17 47 5q2 -1 4.5 -2.5t4 -4t2.5 -4t2 -5t1.5 -5t0.5 -6v-5.5v-6v-7v-456q0 -22 25 -31t50 0.5t25 30.5 v203q0 15 20 28.5t41 19.5l339 131v293l-89 100h-503z" />
<glyph unicode="&#xe131;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM914 632l-275 223q-16 13 -27.5 8t-11.5 -26v-137h-275 q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h275v-137q0 -21 11.5 -26t27.5 8l275 223q16 13 16 32t-16 32z" />
<glyph unicode="&#xe132;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM561 855l-275 -223q-16 -13 -16 -32t16 -32l275 -223q16 -13 27.5 -8 t11.5 26v137h275q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5h-275v137q0 21 -11.5 26t-27.5 -8z" />
<glyph unicode="&#xe133;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM855 639l-223 275q-13 16 -32 16t-32 -16l-223 -275q-13 -16 -8 -27.5 t26 -11.5h137v-275q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v275h137q21 0 26 11.5t-8 27.5z" />
<glyph unicode="&#xe134;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM675 900h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-275h-137q-21 0 -26 -11.5 t8 -27.5l223 -275q13 -16 32 -16t32 16l223 275q13 16 8 27.5t-26 11.5h-137v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe135;" d="M600 1176q116 0 222.5 -46t184 -123.5t123.5 -184t46 -222.5t-46 -222.5t-123.5 -184t-184 -123.5t-222.5 -46t-222.5 46t-184 123.5t-123.5 184t-46 222.5t46 222.5t123.5 184t184 123.5t222.5 46zM627 1101q-15 -12 -36.5 -20.5t-35.5 -12t-43 -8t-39 -6.5 q-15 -3 -45.5 0t-45.5 -2q-20 -7 -51.5 -26.5t-34.5 -34.5q-3 -11 6.5 -22.5t8.5 -18.5q-3 -34 -27.5 -91t-29.5 -79q-9 -34 5 -93t8 -87q0 -9 17 -44.5t16 -59.5q12 0 23 -5t23.5 -15t19.5 -14q16 -8 33 -15t40.5 -15t34.5 -12q21 -9 52.5 -32t60 -38t57.5 -11 q7 -15 -3 -34t-22.5 -40t-9.5 -38q13 -21 23 -34.5t27.5 -27.5t36.5 -18q0 -7 -3.5 -16t-3.5 -14t5 -17q104 -2 221 112q30 29 46.5 47t34.5 49t21 63q-13 8 -37 8.5t-36 7.5q-15 7 -49.5 15t-51.5 19q-18 0 -41 -0.5t-43 -1.5t-42 -6.5t-38 -16.5q-51 -35 -66 -12 q-4 1 -3.5 25.5t0.5 25.5q-6 13 -26.5 17.5t-24.5 6.5q1 15 -0.5 30.5t-7 28t-18.5 11.5t-31 -21q-23 -25 -42 4q-19 28 -8 58q6 16 22 22q6 -1 26 -1.5t33.5 -4t19.5 -13.5q7 -12 18 -24t21.5 -20.5t20 -15t15.5 -10.5l5 -3q2 12 7.5 30.5t8 34.5t-0.5 32q-3 18 3.5 29 t18 22.5t15.5 24.5q6 14 10.5 35t8 31t15.5 22.5t34 22.5q-6 18 10 36q8 0 24 -1.5t24.5 -1.5t20 4.5t20.5 15.5q-10 23 -31 42.5t-37.5 29.5t-49 27t-43.5 23q0 1 2 8t3 11.5t1.5 10.5t-1 9.5t-4.5 4.5q31 -13 58.5 -14.5t38.5 2.5l12 5q5 28 -9.5 46t-36.5 24t-50 15 t-41 20q-18 -4 -37 0zM613 994q0 -17 8 -42t17 -45t9 -23q-8 1 -39.5 5.5t-52.5 10t-37 16.5q3 11 16 29.5t16 25.5q10 -10 19 -10t14 6t13.5 14.5t16.5 12.5z" />
<glyph unicode="&#xe136;" d="M756 1157q164 92 306 -9l-259 -138l145 -232l251 126q6 -89 -34 -156.5t-117 -110.5q-60 -34 -127 -39.5t-126 16.5l-596 -596q-15 -16 -36.5 -16t-36.5 16l-111 110q-15 15 -15 36.5t15 37.5l600 599q-34 101 5.5 201.5t135.5 154.5z" />
<glyph unicode="&#xe137;" horiz-adv-x="1220" d="M100 1196h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 1096h-200v-100h200v100zM100 796h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 696h-500v-100h500v100zM100 396h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 296h-300v-100h300v100z " />
<glyph unicode="&#xe138;" d="M150 1200h900q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM700 500v-300l-200 -200v500l-350 500h900z" />
<glyph unicode="&#xe139;" d="M500 1200h200q41 0 70.5 -29.5t29.5 -70.5v-100h300q41 0 70.5 -29.5t29.5 -70.5v-400h-500v100h-200v-100h-500v400q0 41 29.5 70.5t70.5 29.5h300v100q0 41 29.5 70.5t70.5 29.5zM500 1100v-100h200v100h-200zM1200 400v-200q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v200h1200z" />
<glyph unicode="&#xe140;" d="M50 1200h300q21 0 25 -10.5t-10 -24.5l-94 -94l199 -199q7 -8 7 -18t-7 -18l-106 -106q-8 -7 -18 -7t-18 7l-199 199l-94 -94q-14 -14 -24.5 -10t-10.5 25v300q0 21 14.5 35.5t35.5 14.5zM850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-199 -199q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l199 199l-94 94q-14 14 -10 24.5t25 10.5zM364 470l106 -106q7 -8 7 -18t-7 -18l-199 -199l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l199 199 q8 7 18 7t18 -7zM1071 271l94 94q14 14 24.5 10t10.5 -25v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -25 10.5t10 24.5l94 94l-199 199q-7 8 -7 18t7 18l106 106q8 7 18 7t18 -7z" />
<glyph unicode="&#xe141;" d="M596 1192q121 0 231.5 -47.5t190 -127t127 -190t47.5 -231.5t-47.5 -231.5t-127 -190.5t-190 -127t-231.5 -47t-231.5 47t-190.5 127t-127 190.5t-47 231.5t47 231.5t127 190t190.5 127t231.5 47.5zM596 1010q-112 0 -207.5 -55.5t-151 -151t-55.5 -207.5t55.5 -207.5 t151 -151t207.5 -55.5t207.5 55.5t151 151t55.5 207.5t-55.5 207.5t-151 151t-207.5 55.5zM454.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38.5 -16.5t-38.5 16.5t-16 39t16 38.5t38.5 16zM754.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38 -16.5q-14 0 -29 10l-55 -145 q17 -23 17 -51q0 -36 -25.5 -61.5t-61.5 -25.5t-61.5 25.5t-25.5 61.5q0 32 20.5 56.5t51.5 29.5l122 126l1 1q-9 14 -9 28q0 23 16 39t38.5 16zM345.5 709q22.5 0 38.5 -16t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16zM854.5 709q22.5 0 38.5 -16 t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16z" />
<glyph unicode="&#xe142;" d="M546 173l469 470q91 91 99 192q7 98 -52 175.5t-154 94.5q-22 4 -47 4q-34 0 -66.5 -10t-56.5 -23t-55.5 -38t-48 -41.5t-48.5 -47.5q-376 -375 -391 -390q-30 -27 -45 -41.5t-37.5 -41t-32 -46.5t-16 -47.5t-1.5 -56.5q9 -62 53.5 -95t99.5 -33q74 0 125 51l548 548 q36 36 20 75q-7 16 -21.5 26t-32.5 10q-26 0 -50 -23q-13 -12 -39 -38l-341 -338q-15 -15 -35.5 -15.5t-34.5 13.5t-14 34.5t14 34.5q327 333 361 367q35 35 67.5 51.5t78.5 16.5q14 0 29 -1q44 -8 74.5 -35.5t43.5 -68.5q14 -47 2 -96.5t-47 -84.5q-12 -11 -32 -32 t-79.5 -81t-114.5 -115t-124.5 -123.5t-123 -119.5t-96.5 -89t-57 -45q-56 -27 -120 -27q-70 0 -129 32t-93 89q-48 78 -35 173t81 163l511 511q71 72 111 96q91 55 198 55q80 0 152 -33q78 -36 129.5 -103t66.5 -154q17 -93 -11 -183.5t-94 -156.5l-482 -476 q-15 -15 -36 -16t-37 14t-17.5 34t14.5 35z" />
<glyph unicode="&#xe143;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104zM896 972q-33 0 -64.5 -19t-56.5 -46t-47.5 -53.5t-43.5 -45.5t-37.5 -19t-36 19t-40 45.5t-43 53.5t-54 46t-65.5 19q-67 0 -122.5 -55.5t-55.5 -132.5q0 -23 13.5 -51t46 -65t57.5 -63t76 -75l22 -22q15 -14 44 -44t50.5 -51t46 -44t41 -35t23 -12 t23.5 12t42.5 36t46 44t52.5 52t44 43q4 4 12 13q43 41 63.5 62t52 55t46 55t26 46t11.5 44q0 79 -53 133.5t-120 54.5z" />
<glyph unicode="&#xe144;" d="M776.5 1214q93.5 0 159.5 -66l141 -141q66 -66 66 -160q0 -42 -28 -95.5t-62 -87.5l-29 -29q-31 53 -77 99l-18 18l95 95l-247 248l-389 -389l212 -212l-105 -106l-19 18l-141 141q-66 66 -66 159t66 159l283 283q65 66 158.5 66zM600 706l105 105q10 -8 19 -17l141 -141 q66 -66 66 -159t-66 -159l-283 -283q-66 -66 -159 -66t-159 66l-141 141q-66 66 -66 159.5t66 159.5l55 55q29 -55 75 -102l18 -17l-95 -95l247 -248l389 389z" />
<glyph unicode="&#xe145;" d="M603 1200q85 0 162 -15t127 -38t79 -48t29 -46v-953q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-41 0 -70.5 29.5t-29.5 70.5v953q0 21 30 46.5t81 48t129 37.5t163 15zM300 1000v-700h600v700h-600zM600 254q-43 0 -73.5 -30.5t-30.5 -73.5t30.5 -73.5t73.5 -30.5t73.5 30.5 t30.5 73.5t-30.5 73.5t-73.5 30.5z" />
<glyph unicode="&#xe146;" d="M902 1185l283 -282q15 -15 15 -36t-14.5 -35.5t-35.5 -14.5t-35 15l-36 35l-279 -267v-300l-212 210l-308 -307l-280 -203l203 280l307 308l-210 212h300l267 279l-35 36q-15 14 -15 35t14.5 35.5t35.5 14.5t35 -15z" />
<glyph unicode="&#xe148;" d="M700 1248v-78q38 -5 72.5 -14.5t75.5 -31.5t71 -53.5t52 -84t24 -118.5h-159q-4 36 -10.5 59t-21 45t-40 35.5t-64.5 20.5v-307l64 -13q34 -7 64 -16.5t70 -32t67.5 -52.5t47.5 -80t20 -112q0 -139 -89 -224t-244 -97v-77h-100v79q-150 16 -237 103q-40 40 -52.5 93.5 t-15.5 139.5h139q5 -77 48.5 -126t117.5 -65v335l-27 8q-46 14 -79 26.5t-72 36t-63 52t-40 72.5t-16 98q0 70 25 126t67.5 92t94.5 57t110 27v77h100zM600 754v274q-29 -4 -50 -11t-42 -21.5t-31.5 -41.5t-10.5 -65q0 -29 7 -50.5t16.5 -34t28.5 -22.5t31.5 -14t37.5 -10 q9 -3 13 -4zM700 547v-310q22 2 42.5 6.5t45 15.5t41.5 27t29 42t12 59.5t-12.5 59.5t-38 44.5t-53 31t-66.5 24.5z" />
<glyph unicode="&#xe149;" d="M561 1197q84 0 160.5 -40t123.5 -109.5t47 -147.5h-153q0 40 -19.5 71.5t-49.5 48.5t-59.5 26t-55.5 9q-37 0 -79 -14.5t-62 -35.5q-41 -44 -41 -101q0 -26 13.5 -63t26.5 -61t37 -66q6 -9 9 -14h241v-100h-197q8 -50 -2.5 -115t-31.5 -95q-45 -62 -99 -112 q34 10 83 17.5t71 7.5q32 1 102 -16t104 -17q83 0 136 30l50 -147q-31 -19 -58 -30.5t-55 -15.5t-42 -4.5t-46 -0.5q-23 0 -76 17t-111 32.5t-96 11.5q-39 -3 -82 -16t-67 -25l-23 -11l-55 145q4 3 16 11t15.5 10.5t13 9t15.5 12t14.5 14t17.5 18.5q48 55 54 126.5 t-30 142.5h-221v100h166q-23 47 -44 104q-7 20 -12 41.5t-6 55.5t6 66.5t29.5 70.5t58.5 71q97 88 263 88z" />
<glyph unicode="&#xe150;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM935 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-900h-200v900h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe151;" d="M1000 700h-100v100h-100v-100h-100v500h300v-500zM400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM801 1100v-200h100v200h-100zM1000 350l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150z " />
<glyph unicode="&#xe152;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 1050l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150zM1000 0h-100v100h-100v-100h-100v500h300v-500zM801 400v-200h100v200h-100z " />
<glyph unicode="&#xe153;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 700h-100v400h-100v100h200v-500zM1100 0h-100v100h-200v400h300v-500zM901 400v-200h100v200h-100z" />
<glyph unicode="&#xe154;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1100 700h-100v100h-200v400h300v-500zM901 1100v-200h100v200h-100zM1000 0h-100v400h-100v100h200v-500z" />
<glyph unicode="&#xe155;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM900 1000h-200v200h200v-200zM1000 700h-300v200h300v-200zM1100 400h-400v200h400v-200zM1200 100h-500v200h500v-200z" />
<glyph unicode="&#xe156;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1200 1000h-500v200h500v-200zM1100 700h-400v200h400v-200zM1000 400h-300v200h300v-200zM900 100h-200v200h200v-200z" />
<glyph unicode="&#xe157;" d="M350 1100h400q162 0 256 -93.5t94 -256.5v-400q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5z" />
<glyph unicode="&#xe158;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-163 0 -256.5 92.5t-93.5 257.5v400q0 163 94 256.5t256 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM440 770l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe159;" d="M350 1100h400q163 0 256.5 -94t93.5 -256v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 163 92.5 256.5t257.5 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM350 700h400q21 0 26.5 -12t-6.5 -28l-190 -253q-12 -17 -30 -17t-30 17l-190 253q-12 16 -6.5 28t26.5 12z" />
<glyph unicode="&#xe160;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -163 -92.5 -256.5t-257.5 -93.5h-400q-163 0 -256.5 94t-93.5 256v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM580 693l190 -253q12 -16 6.5 -28t-26.5 -12h-400q-21 0 -26.5 12t6.5 28l190 253q12 17 30 17t30 -17z" />
<glyph unicode="&#xe161;" d="M550 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h450q41 0 70.5 29.5t29.5 70.5v500q0 41 -29.5 70.5t-70.5 29.5h-450q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM338 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe162;" d="M793 1182l9 -9q8 -10 5 -27q-3 -11 -79 -225.5t-78 -221.5l300 1q24 0 32.5 -17.5t-5.5 -35.5q-1 0 -133.5 -155t-267 -312.5t-138.5 -162.5q-12 -15 -26 -15h-9l-9 8q-9 11 -4 32q2 9 42 123.5t79 224.5l39 110h-302q-23 0 -31 19q-10 21 6 41q75 86 209.5 237.5 t228 257t98.5 111.5q9 16 25 16h9z" />
<glyph unicode="&#xe163;" d="M350 1100h400q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-450q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h450q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400 q0 165 92.5 257.5t257.5 92.5zM938 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe164;" d="M750 1200h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -10.5 -25t-24.5 10l-109 109l-312 -312q-15 -15 -35.5 -15t-35.5 15l-141 141q-15 15 -15 35.5t15 35.5l312 312l-109 109q-14 14 -10 24.5t25 10.5zM456 900h-156q-41 0 -70.5 -29.5t-29.5 -70.5v-500 q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v148l200 200v-298q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5h300z" />
<glyph unicode="&#xe165;" d="M600 1186q119 0 227.5 -46.5t187 -125t125 -187t46.5 -227.5t-46.5 -227.5t-125 -187t-187 -125t-227.5 -46.5t-227.5 46.5t-187 125t-125 187t-46.5 227.5t46.5 227.5t125 187t187 125t227.5 46.5zM600 1022q-115 0 -212 -56.5t-153.5 -153.5t-56.5 -212t56.5 -212 t153.5 -153.5t212 -56.5t212 56.5t153.5 153.5t56.5 212t-56.5 212t-153.5 153.5t-212 56.5zM600 794q80 0 137 -57t57 -137t-57 -137t-137 -57t-137 57t-57 137t57 137t137 57z" />
<glyph unicode="&#xe166;" d="M450 1200h200q21 0 35.5 -14.5t14.5 -35.5v-350h245q20 0 25 -11t-9 -26l-383 -426q-14 -15 -33.5 -15t-32.5 15l-379 426q-13 15 -8.5 26t25.5 11h250v350q0 21 14.5 35.5t35.5 14.5zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe167;" d="M583 1182l378 -435q14 -15 9 -31t-26 -16h-244v-250q0 -20 -17 -35t-39 -15h-200q-20 0 -32 14.5t-12 35.5v250h-250q-20 0 -25.5 16.5t8.5 31.5l383 431q14 16 33.5 17t33.5 -14zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe168;" d="M396 723l369 369q7 7 17.5 7t17.5 -7l139 -139q7 -8 7 -18.5t-7 -17.5l-525 -525q-7 -8 -17.5 -8t-17.5 8l-292 291q-7 8 -7 18t7 18l139 139q8 7 18.5 7t17.5 -7zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50 h-100z" />
<glyph unicode="&#xe169;" d="M135 1023l142 142q14 14 35 14t35 -14l77 -77l-212 -212l-77 76q-14 15 -14 36t14 35zM655 855l210 210q14 14 24.5 10t10.5 -25l-2 -599q-1 -20 -15.5 -35t-35.5 -15l-597 -1q-21 0 -25 10.5t10 24.5l208 208l-154 155l212 212zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5 v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe170;" d="M350 1200l599 -2q20 -1 35 -15.5t15 -35.5l1 -597q0 -21 -10.5 -25t-24.5 10l-208 208l-155 -154l-212 212l155 154l-210 210q-14 14 -10 24.5t25 10.5zM524 512l-76 -77q-15 -14 -36 -14t-35 14l-142 142q-14 14 -14 35t14 35l77 77zM50 300h1000q21 0 35.5 -14.5 t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe171;" d="M1200 103l-483 276l-314 -399v423h-399l1196 796v-1096zM483 424v-230l683 953z" />
<glyph unicode="&#xe172;" d="M1100 1000v-850q0 -21 -14.5 -35.5t-35.5 -14.5h-150v400h-700v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200z" />
<glyph unicode="&#xe173;" d="M1100 1000l-2 -149l-299 -299l-95 95q-9 9 -21.5 9t-21.5 -9l-149 -147h-312v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1132 638l106 -106q7 -7 7 -17.5t-7 -17.5l-420 -421q-8 -7 -18 -7 t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l297 297q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe174;" d="M1100 1000v-269l-103 -103l-134 134q-15 15 -33.5 16.5t-34.5 -12.5l-266 -266h-329v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1202 572l70 -70q15 -15 15 -35.5t-15 -35.5l-131 -131 l131 -131q15 -15 15 -35.5t-15 -35.5l-70 -70q-15 -15 -35.5 -15t-35.5 15l-131 131l-131 -131q-15 -15 -35.5 -15t-35.5 15l-70 70q-15 15 -15 35.5t15 35.5l131 131l-131 131q-15 15 -15 35.5t15 35.5l70 70q15 15 35.5 15t35.5 -15l131 -131l131 131q15 15 35.5 15 t35.5 -15z" />
<glyph unicode="&#xe175;" d="M1100 1000v-300h-350q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-500v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM850 600h100q21 0 35.5 -14.5t14.5 -35.5v-250h150q21 0 25 -10.5t-10 -24.5 l-230 -230q-14 -14 -35 -14t-35 14l-230 230q-14 14 -10 24.5t25 10.5h150v250q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe176;" d="M1100 1000v-400l-165 165q-14 15 -35 15t-35 -15l-263 -265h-402v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM935 565l230 -229q14 -15 10 -25.5t-25 -10.5h-150v-250q0 -20 -14.5 -35 t-35.5 -15h-100q-21 0 -35.5 15t-14.5 35v250h-150q-21 0 -25 10.5t10 25.5l230 229q14 15 35 15t35 -15z" />
<glyph unicode="&#xe177;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-150h-1200v150q0 21 14.5 35.5t35.5 14.5zM1200 800v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v550h1200zM100 500v-200h400v200h-400z" />
<glyph unicode="&#xe178;" d="M935 1165l248 -230q14 -14 14 -35t-14 -35l-248 -230q-14 -14 -24.5 -10t-10.5 25v150h-400v200h400v150q0 21 10.5 25t24.5 -10zM200 800h-50q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v-200zM400 800h-100v200h100v-200zM18 435l247 230 q14 14 24.5 10t10.5 -25v-150h400v-200h-400v-150q0 -21 -10.5 -25t-24.5 10l-247 230q-15 14 -15 35t15 35zM900 300h-100v200h100v-200zM1000 500h51q20 0 34.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-34.5 -14.5h-51v200z" />
<glyph unicode="&#xe179;" d="M862 1073l276 116q25 18 43.5 8t18.5 -41v-1106q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v397q-4 1 -11 5t-24 17.5t-30 29t-24 42t-11 56.5v359q0 31 18.5 65t43.5 52zM550 1200q22 0 34.5 -12.5t14.5 -24.5l1 -13v-450q0 -28 -10.5 -59.5 t-25 -56t-29 -45t-25.5 -31.5l-10 -11v-447q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v447q-4 4 -11 11.5t-24 30.5t-30 46t-24 55t-11 60v450q0 2 0.5 5.5t4 12t8.5 15t14.5 12t22.5 5.5q20 0 32.5 -12.5t14.5 -24.5l3 -13v-350h100v350v5.5t2.5 12 t7 15t15 12t25.5 5.5q23 0 35.5 -12.5t13.5 -24.5l1 -13v-350h100v350q0 2 0.5 5.5t3 12t7 15t15 12t24.5 5.5z" />
<glyph unicode="&#xe180;" d="M1200 1100v-56q-4 0 -11 -0.5t-24 -3t-30 -7.5t-24 -15t-11 -24v-888q0 -22 25 -34.5t50 -13.5l25 -2v-56h-400v56q75 0 87.5 6.5t12.5 43.5v394h-500v-394q0 -37 12.5 -43.5t87.5 -6.5v-56h-400v56q4 0 11 0.5t24 3t30 7.5t24 15t11 24v888q0 22 -25 34.5t-50 13.5 l-25 2v56h400v-56q-75 0 -87.5 -6.5t-12.5 -43.5v-394h500v394q0 37 -12.5 43.5t-87.5 6.5v56h400z" />
<glyph unicode="&#xe181;" d="M675 1000h375q21 0 35.5 -14.5t14.5 -35.5v-150h-105l-295 -98v98l-200 200h-400l100 100h375zM100 900h300q41 0 70.5 -29.5t29.5 -70.5v-500q0 -41 -29.5 -70.5t-70.5 -29.5h-300q-41 0 -70.5 29.5t-29.5 70.5v500q0 41 29.5 70.5t70.5 29.5zM100 800v-200h300v200 h-300zM1100 535l-400 -133v163l400 133v-163zM100 500v-200h300v200h-300zM1100 398v-248q0 -21 -14.5 -35.5t-35.5 -14.5h-375l-100 -100h-375l-100 100h400l200 200h105z" />
<glyph unicode="&#xe182;" d="M17 1007l162 162q17 17 40 14t37 -22l139 -194q14 -20 11 -44.5t-20 -41.5l-119 -118q102 -142 228 -268t267 -227l119 118q17 17 42.5 19t44.5 -12l192 -136q19 -14 22.5 -37.5t-13.5 -40.5l-163 -162q-3 -1 -9.5 -1t-29.5 2t-47.5 6t-62.5 14.5t-77.5 26.5t-90 42.5 t-101.5 60t-111 83t-119 108.5q-74 74 -133.5 150.5t-94.5 138.5t-60 119.5t-34.5 100t-15 74.5t-4.5 48z" />
<glyph unicode="&#xe183;" d="M600 1100q92 0 175 -10.5t141.5 -27t108.5 -36.5t81.5 -40t53.5 -37t31 -27l9 -10v-200q0 -21 -14.5 -33t-34.5 -9l-202 34q-20 3 -34.5 20t-14.5 38v146q-141 24 -300 24t-300 -24v-146q0 -21 -14.5 -38t-34.5 -20l-202 -34q-20 -3 -34.5 9t-14.5 33v200q3 4 9.5 10.5 t31 26t54 37.5t80.5 39.5t109 37.5t141 26.5t175 10.5zM600 795q56 0 97 -9.5t60 -23.5t30 -28t12 -24l1 -10v-50l365 -303q14 -15 24.5 -40t10.5 -45v-212q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v212q0 20 10.5 45t24.5 40l365 303v50 q0 4 1 10.5t12 23t30 29t60 22.5t97 10z" />
<glyph unicode="&#xe184;" d="M1100 700l-200 -200h-600l-200 200v500h200v-200h200v200h200v-200h200v200h200v-500zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5 t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe185;" d="M700 1100h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-1000h300v1000q0 41 -29.5 70.5t-70.5 29.5zM1100 800h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-700h300v700q0 41 -29.5 70.5t-70.5 29.5zM400 0h-300v400q0 41 29.5 70.5t70.5 29.5h100q41 0 70.5 -29.5t29.5 -70.5v-400z " />
<glyph unicode="&#xe186;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe187;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 300h-100v200h-100v-200h-100v500h100v-200h100v200h100v-500zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe188;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-300h200v-100h-300v500h300v-100zM900 700h-200v-300h200v-100h-300v500h300v-100z" />
<glyph unicode="&#xe189;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 400l-300 150l300 150v-300zM900 550l-300 -150v300z" />
<glyph unicode="&#xe190;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM900 300h-700v500h700v-500zM800 700h-130q-38 0 -66.5 -43t-28.5 -108t27 -107t68 -42h130v300zM300 700v-300 h130q41 0 68 42t27 107t-28.5 108t-66.5 43h-130z" />
<glyph unicode="&#xe191;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 300h-100v400h-100v100h200v-500z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe192;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM300 700h200v-400h-300v500h100v-100zM900 300h-100v400h-100v100h200v-500zM300 600v-200h100v200h-100z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe193;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 500l-199 -200h-100v50l199 200v150h-200v100h300v-300zM900 300h-100v400h-100v100h200v-500zM701 300h-100 v100h100v-100z" />
<glyph unicode="&#xe194;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700h-300v-200h300v-100h-300l-100 100v200l100 100h300v-100z" />
<glyph unicode="&#xe195;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700v-100l-50 -50l100 -100v-50h-100l-100 100h-150v-100h-100v400h300zM500 700v-100h200v100h-200z" />
<glyph unicode="&#xe197;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -207t-85 -207t-205 -86.5h-128v250q0 21 -14.5 35.5t-35.5 14.5h-300q-21 0 -35.5 -14.5t-14.5 -35.5v-250h-222q-80 0 -136 57.5t-56 136.5q0 69 43 122.5t108 67.5q-2 19 -2 37q0 100 49 185 t134 134t185 49zM525 500h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -244q-13 -16 -32 -16t-32 16l-223 244q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe198;" d="M502 1089q110 0 201 -59.5t135 -156.5q43 15 89 15q121 0 206 -86.5t86 -206.5q0 -99 -60 -181t-150 -110l-378 360q-13 16 -31.5 16t-31.5 -16l-381 -365h-9q-79 0 -135.5 57.5t-56.5 136.5q0 69 43 122.5t108 67.5q-2 19 -2 38q0 100 49 184.5t133.5 134t184.5 49.5z M632 467l223 -228q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5q199 204 223 228q19 19 31.5 19t32.5 -19z" />
<glyph unicode="&#xe199;" d="M700 100v100h400l-270 300h170l-270 300h170l-300 333l-300 -333h170l-270 -300h170l-270 -300h400v-100h-50q-21 0 -35.5 -14.5t-14.5 -35.5v-50h400v50q0 21 -14.5 35.5t-35.5 14.5h-50z" />
<glyph unicode="&#xe200;" d="M600 1179q94 0 167.5 -56.5t99.5 -145.5q89 -6 150.5 -71.5t61.5 -155.5q0 -61 -29.5 -112.5t-79.5 -82.5q9 -29 9 -55q0 -74 -52.5 -126.5t-126.5 -52.5q-55 0 -100 30v-251q21 0 35.5 -14.5t14.5 -35.5v-50h-300v50q0 21 14.5 35.5t35.5 14.5v251q-45 -30 -100 -30 q-74 0 -126.5 52.5t-52.5 126.5q0 18 4 38q-47 21 -75.5 65t-28.5 97q0 74 52.5 126.5t126.5 52.5q5 0 23 -2q0 2 -1 10t-1 13q0 116 81.5 197.5t197.5 81.5z" />
<glyph unicode="&#xe201;" d="M1010 1010q111 -111 150.5 -260.5t0 -299t-150.5 -260.5q-83 -83 -191.5 -126.5t-218.5 -43.5t-218.5 43.5t-191.5 126.5q-111 111 -150.5 260.5t0 299t150.5 260.5q83 83 191.5 126.5t218.5 43.5t218.5 -43.5t191.5 -126.5zM476 1065q-4 0 -8 -1q-121 -34 -209.5 -122.5 t-122.5 -209.5q-4 -12 2.5 -23t18.5 -14l36 -9q3 -1 7 -1q23 0 29 22q27 96 98 166q70 71 166 98q11 3 17.5 13.5t3.5 22.5l-9 35q-3 13 -14 19q-7 4 -15 4zM512 920q-4 0 -9 -2q-80 -24 -138.5 -82.5t-82.5 -138.5q-4 -13 2 -24t19 -14l34 -9q4 -1 8 -1q22 0 28 21 q18 58 58.5 98.5t97.5 58.5q12 3 18 13.5t3 21.5l-9 35q-3 12 -14 19q-7 4 -15 4zM719.5 719.5q-49.5 49.5 -119.5 49.5t-119.5 -49.5t-49.5 -119.5t49.5 -119.5t119.5 -49.5t119.5 49.5t49.5 119.5t-49.5 119.5zM855 551q-22 0 -28 -21q-18 -58 -58.5 -98.5t-98.5 -57.5 q-11 -4 -17 -14.5t-3 -21.5l9 -35q3 -12 14 -19q7 -4 15 -4q4 0 9 2q80 24 138.5 82.5t82.5 138.5q4 13 -2.5 24t-18.5 14l-34 9q-4 1 -8 1zM1000 515q-23 0 -29 -22q-27 -96 -98 -166q-70 -71 -166 -98q-11 -3 -17.5 -13.5t-3.5 -22.5l9 -35q3 -13 14 -19q7 -4 15 -4 q4 0 8 1q121 34 209.5 122.5t122.5 209.5q4 12 -2.5 23t-18.5 14l-36 9q-3 1 -7 1z" />
<glyph unicode="&#xe202;" d="M700 800h300v-380h-180v200h-340v-200h-380v755q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM700 300h162l-212 -212l-212 212h162v200h100v-200zM520 0h-395q-10 0 -17.5 7.5t-7.5 17.5v395zM1000 220v-195q0 -10 -7.5 -17.5t-17.5 -7.5h-195z" />
<glyph unicode="&#xe203;" d="M700 800h300v-520l-350 350l-550 -550v1095q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM862 200h-162v-200h-100v200h-162l212 212zM480 0h-355q-10 0 -17.5 7.5t-7.5 17.5v55h380v-80zM1000 80v-55q0 -10 -7.5 -17.5t-17.5 -7.5h-155v80h180z" />
<glyph unicode="&#xe204;" d="M1162 800h-162v-200h100l100 -100h-300v300h-162l212 212zM200 800h200q27 0 40 -2t29.5 -10.5t23.5 -30t7 -57.5h300v-100h-600l-200 -350v450h100q0 36 7 57.5t23.5 30t29.5 10.5t40 2zM800 400h240l-240 -400h-800l300 500h500v-100z" />
<glyph unicode="&#xe205;" d="M650 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM1000 850v150q41 0 70.5 -29.5t29.5 -70.5v-800 q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-1 0 -20 4l246 246l-326 326v324q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM412 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe206;" d="M450 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM800 850v150q41 0 70.5 -29.5t29.5 -70.5v-500 h-200v-300h200q0 -36 -7 -57.5t-23.5 -30t-29.5 -10.5t-40 -2h-600q-41 0 -70.5 29.5t-29.5 70.5v800q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM1212 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe209;" d="M658 1197l637 -1104q23 -38 7 -65.5t-60 -27.5h-1276q-44 0 -60 27.5t7 65.5l637 1104q22 39 54 39t54 -39zM704 800h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM500 300v-100h200 v100h-200z" />
<glyph unicode="&#xe210;" d="M425 1100h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM825 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM25 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5zM425 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5 v150q0 10 7.5 17.5t17.5 7.5zM25 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe211;" d="M700 1200h100v-200h-100v-100h350q62 0 86.5 -39.5t-3.5 -94.5l-66 -132q-41 -83 -81 -134h-772q-40 51 -81 134l-66 132q-28 55 -3.5 94.5t86.5 39.5h350v100h-100v200h100v100h200v-100zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100 h-950l138 100h-13q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe212;" d="M600 1300q40 0 68.5 -29.5t28.5 -70.5h-194q0 41 28.5 70.5t68.5 29.5zM443 1100h314q18 -37 18 -75q0 -8 -3 -25h328q41 0 44.5 -16.5t-30.5 -38.5l-175 -145h-678l-178 145q-34 22 -29 38.5t46 16.5h328q-3 17 -3 25q0 38 18 75zM250 700h700q21 0 35.5 -14.5 t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-150v-200l275 -200h-950l275 200v200h-150q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe213;" d="M600 1181q75 0 128 -53t53 -128t-53 -128t-128 -53t-128 53t-53 128t53 128t128 53zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13 l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe214;" d="M600 1300q47 0 92.5 -53.5t71 -123t25.5 -123.5q0 -78 -55.5 -133.5t-133.5 -55.5t-133.5 55.5t-55.5 133.5q0 62 34 143l144 -143l111 111l-163 163q34 26 63 26zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45 zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe215;" d="M600 1200l300 -161v-139h-300q0 -57 18.5 -108t50 -91.5t63 -72t70 -67.5t57.5 -61h-530q-60 83 -90.5 177.5t-30.5 178.5t33 164.5t87.5 139.5t126 96.5t145.5 41.5v-98zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100 h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe216;" d="M600 1300q41 0 70.5 -29.5t29.5 -70.5v-78q46 -26 73 -72t27 -100v-50h-400v50q0 54 27 100t73 72v78q0 41 29.5 70.5t70.5 29.5zM400 800h400q54 0 100 -27t72 -73h-172v-100h200v-100h-200v-100h200v-100h-200v-100h200q0 -83 -58.5 -141.5t-141.5 -58.5h-400 q-83 0 -141.5 58.5t-58.5 141.5v400q0 83 58.5 141.5t141.5 58.5z" />
<glyph unicode="&#xe218;" d="M150 1100h900q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM125 400h950q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-283l224 -224q13 -13 13 -31.5t-13 -32 t-31.5 -13.5t-31.5 13l-88 88h-524l-87 -88q-13 -13 -32 -13t-32 13.5t-13 32t13 31.5l224 224h-289q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM541 300l-100 -100h324l-100 100h-124z" />
<glyph unicode="&#xe219;" d="M200 1100h800q83 0 141.5 -58.5t58.5 -141.5v-200h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100v200q0 83 58.5 141.5t141.5 58.5zM100 600h1000q41 0 70.5 -29.5 t29.5 -70.5v-300h-1200v300q0 41 29.5 70.5t70.5 29.5zM300 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200zM1100 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200z" />
<glyph unicode="&#xe221;" d="M480 1165l682 -683q31 -31 31 -75.5t-31 -75.5l-131 -131h-481l-517 518q-32 31 -32 75.5t32 75.5l295 296q31 31 75.5 31t76.5 -31zM108 794l342 -342l303 304l-341 341zM250 100h800q21 0 35.5 -14.5t14.5 -35.5v-50h-900v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe223;" d="M1057 647l-189 506q-8 19 -27.5 33t-40.5 14h-400q-21 0 -40.5 -14t-27.5 -33l-189 -506q-8 -19 1.5 -33t30.5 -14h625v-150q0 -21 14.5 -35.5t35.5 -14.5t35.5 14.5t14.5 35.5v150h125q21 0 30.5 14t1.5 33zM897 0h-595v50q0 21 14.5 35.5t35.5 14.5h50v50 q0 21 14.5 35.5t35.5 14.5h48v300h200v-300h47q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-50z" />
<glyph unicode="&#xe224;" d="M900 800h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-375v591l-300 300v84q0 10 7.5 17.5t17.5 7.5h375v-400zM1200 900h-200v200zM400 600h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-650q-10 0 -17.5 7.5t-7.5 17.5v950q0 10 7.5 17.5t17.5 7.5h375v-400zM700 700h-200v200z " />
<glyph unicode="&#xe225;" d="M484 1095h195q75 0 146 -32.5t124 -86t89.5 -122.5t48.5 -142q18 -14 35 -20q31 -10 64.5 6.5t43.5 48.5q10 34 -15 71q-19 27 -9 43q5 8 12.5 11t19 -1t23.5 -16q41 -44 39 -105q-3 -63 -46 -106.5t-104 -43.5h-62q-7 -55 -35 -117t-56 -100l-39 -234q-3 -20 -20 -34.5 t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l12 70q-49 -14 -91 -14h-195q-24 0 -65 8l-11 -64q-3 -20 -20 -34.5t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l26 157q-84 74 -128 175l-159 53q-19 7 -33 26t-14 40v50q0 21 14.5 35.5t35.5 14.5h124q11 87 56 166l-111 95 q-16 14 -12.5 23.5t24.5 9.5h203q116 101 250 101zM675 1000h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h250q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe226;" d="M641 900l423 247q19 8 42 2.5t37 -21.5l32 -38q14 -15 12.5 -36t-17.5 -34l-139 -120h-390zM50 1100h106q67 0 103 -17t66 -71l102 -212h823q21 0 35.5 -14.5t14.5 -35.5v-50q0 -21 -14 -40t-33 -26l-737 -132q-23 -4 -40 6t-26 25q-42 67 -100 67h-300q-62 0 -106 44 t-44 106v200q0 62 44 106t106 44zM173 928h-80q-19 0 -28 -14t-9 -35v-56q0 -51 42 -51h134q16 0 21.5 8t5.5 24q0 11 -16 45t-27 51q-18 28 -43 28zM550 727q-32 0 -54.5 -22.5t-22.5 -54.5t22.5 -54.5t54.5 -22.5t54.5 22.5t22.5 54.5t-22.5 54.5t-54.5 22.5zM130 389 l152 130q18 19 34 24t31 -3.5t24.5 -17.5t25.5 -28q28 -35 50.5 -51t48.5 -13l63 5l48 -179q13 -61 -3.5 -97.5t-67.5 -79.5l-80 -69q-47 -40 -109 -35.5t-103 51.5l-130 151q-40 47 -35.5 109.5t51.5 102.5zM380 377l-102 -88q-31 -27 2 -65l37 -43q13 -15 27.5 -19.5 t31.5 6.5l61 53q19 16 14 49q-2 20 -12 56t-17 45q-11 12 -19 14t-23 -8z" />
<glyph unicode="&#xe227;" d="M625 1200h150q10 0 17.5 -7.5t7.5 -17.5v-109q79 -33 131 -87.5t53 -128.5q1 -46 -15 -84.5t-39 -61t-46 -38t-39 -21.5l-17 -6q6 0 15 -1.5t35 -9t50 -17.5t53 -30t50 -45t35.5 -64t14.5 -84q0 -59 -11.5 -105.5t-28.5 -76.5t-44 -51t-49.5 -31.5t-54.5 -16t-49.5 -6.5 t-43.5 -1v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-100v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-175q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v600h-75q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5h175v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h100v75q0 10 7.5 17.5t17.5 7.5zM400 900v-200h263q28 0 48.5 10.5t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-263zM400 500v-200h363q28 0 48.5 10.5 t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-363z" />
<glyph unicode="&#xe230;" d="M212 1198h780q86 0 147 -61t61 -147v-416q0 -51 -18 -142.5t-36 -157.5l-18 -66q-29 -87 -93.5 -146.5t-146.5 -59.5h-572q-82 0 -147 59t-93 147q-8 28 -20 73t-32 143.5t-20 149.5v416q0 86 61 147t147 61zM600 1045q-70 0 -132.5 -11.5t-105.5 -30.5t-78.5 -41.5 t-57 -45t-36 -41t-20.5 -30.5l-6 -12l156 -243h560l156 243q-2 5 -6 12.5t-20 29.5t-36.5 42t-57 44.5t-79 42t-105 29.5t-132.5 12zM762 703h-157l195 261z" />
<glyph unicode="&#xe231;" d="M475 1300h150q103 0 189 -86t86 -189v-500q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe232;" d="M475 1300h96q0 -150 89.5 -239.5t239.5 -89.5v-446q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe233;" d="M1294 767l-638 -283l-378 170l-78 -60v-224l100 -150v-199l-150 148l-150 -149v200l100 150v250q0 4 -0.5 10.5t0 9.5t1 8t3 8t6.5 6l47 40l-147 65l642 283zM1000 380l-350 -166l-350 166v147l350 -165l350 165v-147z" />
<glyph unicode="&#xe234;" d="M250 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM650 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM1050 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe235;" d="M550 1100q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 700q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 300q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe236;" d="M125 1100h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM125 700h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM125 300h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe237;" d="M350 1200h500q162 0 256 -93.5t94 -256.5v-500q0 -165 -93.5 -257.5t-256.5 -92.5h-500q-165 0 -257.5 92.5t-92.5 257.5v500q0 165 92.5 257.5t257.5 92.5zM900 1000h-600q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h600q41 0 70.5 29.5 t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5zM350 900h500q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-500q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 14.5 35.5t35.5 14.5zM400 800v-200h400v200h-400z" />
<glyph unicode="&#xe238;" d="M150 1100h1000q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe239;" d="M650 1187q87 -67 118.5 -156t0 -178t-118.5 -155q-87 66 -118.5 155t0 178t118.5 156zM300 800q124 0 212 -88t88 -212q-124 0 -212 88t-88 212zM1000 800q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM300 500q124 0 212 -88t88 -212q-124 0 -212 88t-88 212z M1000 500q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM700 199v-144q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v142q40 -4 43 -4q17 0 57 6z" />
<glyph unicode="&#xe240;" d="M745 878l69 19q25 6 45 -12l298 -295q11 -11 15 -26.5t-2 -30.5q-5 -14 -18 -23.5t-28 -9.5h-8q1 0 1 -13q0 -29 -2 -56t-8.5 -62t-20 -63t-33 -53t-51 -39t-72.5 -14h-146q-184 0 -184 288q0 24 10 47q-20 4 -62 4t-63 -4q11 -24 11 -47q0 -288 -184 -288h-142 q-48 0 -84.5 21t-56 51t-32 71.5t-16 75t-3.5 68.5q0 13 2 13h-7q-15 0 -27.5 9.5t-18.5 23.5q-6 15 -2 30.5t15 25.5l298 296q20 18 46 11l76 -19q20 -5 30.5 -22.5t5.5 -37.5t-22.5 -31t-37.5 -5l-51 12l-182 -193h891l-182 193l-44 -12q-20 -5 -37.5 6t-22.5 31t6 37.5 t31 22.5z" />
<glyph unicode="&#xe241;" d="M1200 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM500 450h-25q0 15 -4 24.5t-9 14.5t-17 7.5t-20 3t-25 0.5h-100v-425q0 -11 12.5 -17.5t25.5 -7.5h12v-50h-200v50q50 0 50 25v425h-100q-17 0 -25 -0.5t-20 -3t-17 -7.5t-9 -14.5t-4 -24.5h-25v150h500v-150z" />
<glyph unicode="&#xe242;" d="M1000 300v50q-25 0 -55 32q-14 14 -25 31t-16 27l-4 11l-289 747h-69l-300 -754q-18 -35 -39 -56q-9 -9 -24.5 -18.5t-26.5 -14.5l-11 -5v-50h273v50q-49 0 -78.5 21.5t-11.5 67.5l69 176h293l61 -166q13 -34 -3.5 -66.5t-55.5 -32.5v-50h312zM412 691l134 342l121 -342 h-255zM1100 150v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe243;" d="M50 1200h1100q21 0 35.5 -14.5t14.5 -35.5v-1100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5zM611 1118h-70q-13 0 -18 -12l-299 -753q-17 -32 -35 -51q-18 -18 -56 -34q-12 -5 -12 -18v-50q0 -8 5.5 -14t14.5 -6 h273q8 0 14 6t6 14v50q0 8 -6 14t-14 6q-55 0 -71 23q-10 14 0 39l63 163h266l57 -153q11 -31 -6 -55q-12 -17 -36 -17q-8 0 -14 -6t-6 -14v-50q0 -8 6 -14t14 -6h313q8 0 14 6t6 14v50q0 7 -5.5 13t-13.5 7q-17 0 -42 25q-25 27 -40 63h-1l-288 748q-5 12 -19 12zM639 611 h-197l103 264z" />
<glyph unicode="&#xe244;" d="M1200 1100h-1200v100h1200v-100zM50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 1000h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM700 900v-300h300v300h-300z" />
<glyph unicode="&#xe245;" d="M50 1200h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 700h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM700 600v-300h300v300h-300zM1200 0h-1200v100h1200v-100z" />
<glyph unicode="&#xe246;" d="M50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-350h100v150q0 21 14.5 35.5t35.5 14.5h400q21 0 35.5 -14.5t14.5 -35.5v-150h100v-100h-100v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v150h-100v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM700 700v-300h300v300h-300z" />
<glyph unicode="&#xe247;" d="M100 0h-100v1200h100v-1200zM250 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM300 1000v-300h300v300h-300zM250 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe248;" d="M600 1100h150q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-100h450q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h350v100h-150q-21 0 -35.5 14.5 t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h150v100h100v-100zM400 1000v-300h300v300h-300z" />
<glyph unicode="&#xe249;" d="M1200 0h-100v1200h100v-1200zM550 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM600 1000v-300h300v300h-300zM50 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe250;" d="M865 565l-494 -494q-23 -23 -41 -23q-14 0 -22 13.5t-8 38.5v1000q0 25 8 38.5t22 13.5q18 0 41 -23l494 -494q14 -14 14 -35t-14 -35z" />
<glyph unicode="&#xe251;" d="M335 635l494 494q29 29 50 20.5t21 -49.5v-1000q0 -41 -21 -49.5t-50 20.5l-494 494q-14 14 -14 35t14 35z" />
<glyph unicode="&#xe252;" d="M100 900h1000q41 0 49.5 -21t-20.5 -50l-494 -494q-14 -14 -35 -14t-35 14l-494 494q-29 29 -20.5 50t49.5 21z" />
<glyph unicode="&#xe253;" d="M635 865l494 -494q29 -29 20.5 -50t-49.5 -21h-1000q-41 0 -49.5 21t20.5 50l494 494q14 14 35 14t35 -14z" />
<glyph unicode="&#xe254;" d="M700 741v-182l-692 -323v221l413 193l-413 193v221zM1200 0h-800v200h800v-200z" />
<glyph unicode="&#xe255;" d="M1200 900h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300zM0 700h50q0 21 4 37t9.5 26.5t18 17.5t22 11t28.5 5.5t31 2t37 0.5h100v-550q0 -22 -25 -34.5t-50 -13.5l-25 -2v-100h400v100q-4 0 -11 0.5t-24 3t-30 7t-24 15t-11 24.5v550h100q25 0 37 -0.5t31 -2 t28.5 -5.5t22 -11t18 -17.5t9.5 -26.5t4 -37h50v300h-800v-300z" />
<glyph unicode="&#xe256;" d="M800 700h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-100v-550q0 -22 25 -34.5t50 -14.5l25 -1v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v550h-100q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h800v-300zM1100 200h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300z" />
<glyph unicode="&#xe257;" d="M701 1098h160q16 0 21 -11t-7 -23l-464 -464l464 -464q12 -12 7 -23t-21 -11h-160q-13 0 -23 9l-471 471q-7 8 -7 18t7 18l471 471q10 9 23 9z" />
<glyph unicode="&#xe258;" d="M339 1098h160q13 0 23 -9l471 -471q7 -8 7 -18t-7 -18l-471 -471q-10 -9 -23 -9h-160q-16 0 -21 11t7 23l464 464l-464 464q-12 12 -7 23t21 11z" />
<glyph unicode="&#xe259;" d="M1087 882q11 -5 11 -21v-160q0 -13 -9 -23l-471 -471q-8 -7 -18 -7t-18 7l-471 471q-9 10 -9 23v160q0 16 11 21t23 -7l464 -464l464 464q12 12 23 7z" />
<glyph unicode="&#xe260;" d="M618 993l471 -471q9 -10 9 -23v-160q0 -16 -11 -21t-23 7l-464 464l-464 -464q-12 -12 -23 -7t-11 21v160q0 13 9 23l471 471q8 7 18 7t18 -7z" />
<glyph unicode="&#xf8ff;" d="M1000 1200q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM450 1000h100q21 0 40 -14t26 -33l79 -194q5 1 16 3q34 6 54 9.5t60 7t65.5 1t61 -10t56.5 -23t42.5 -42t29 -64t5 -92t-19.5 -121.5q-1 -7 -3 -19.5t-11 -50t-20.5 -73t-32.5 -81.5t-46.5 -83t-64 -70 t-82.5 -50q-13 -5 -42 -5t-65.5 2.5t-47.5 2.5q-14 0 -49.5 -3.5t-63 -3.5t-43.5 7q-57 25 -104.5 78.5t-75 111.5t-46.5 112t-26 90l-7 35q-15 63 -18 115t4.5 88.5t26 64t39.5 43.5t52 25.5t58.5 13t62.5 2t59.5 -4.5t55.5 -8l-147 192q-12 18 -5.5 30t27.5 12z" />
<glyph unicode="&#x1f511;" d="M250 1200h600q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-500l-255 -178q-19 -9 -32 -1t-13 29v650h-150q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM400 1100v-100h300v100h-300z" />
<glyph unicode="&#x1f6aa;" d="M250 1200h750q39 0 69.5 -40.5t30.5 -84.5v-933l-700 -117v950l600 125h-700v-1000h-100v1025q0 23 15.5 49t34.5 26zM500 525v-100l100 20v100z" />
</font>
</defs></svg> ) format('svg')}.glyphicon{position:relative;top:1px;display:inline-block;font-family:'Glyphicons Halflings';font-style:normal;font-weight:400;line-height:1;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}.glyphicon-asterisk:before{content:"\2a"}.glyphicon-plus:before{content:"\2b"}.glyphicon-eur:before,.glyphicon-euro:before{content:"\20ac"}.glyphicon-minus:before{content:"\2212"}.glyphicon-cloud:before{content:"\2601"}.glyphicon-envelope:before{content:"\2709"}.glyphicon-pencil:before{content:"\270f"}.glyphicon-glass:before{content:"\e001"}.glyphicon-music:before{content:"\e002"}.glyphicon-search:before{content:"\e003"}.glyphicon-heart:before{content:"\e005"}.glyphicon-star:before{content:"\e006"}.glyphicon-star-empty:before{content:"\e007"}.glyphicon-user:before{content:"\e008"}.glyphicon-film:before{content:"\e009"}.glyphicon-th-large:before{content:"\e010"}.glyphicon-th:before{content:"\e011"}.glyphicon-th-list:before{content:"\e012"}.glyphicon-ok:before{content:"\e013"}.glyphicon-remove:before{content:"\e014"}.glyphicon-zoom-in:before{content:"\e015"}.glyphicon-zoom-out:before{content:"\e016"}.glyphicon-off:before{content:"\e017"}.glyphicon-signal:before{content:"\e018"}.glyphicon-cog:before{content:"\e019"}.glyphicon-trash:before{content:"\e020"}.glyphicon-home:before{content:"\e021"}.glyphicon-file:before{content:"\e022"}.glyphicon-time:before{content:"\e023"}.glyphicon-road:before{content:"\e024"}.glyphicon-download-alt:before{content:"\e025"}.glyphicon-download:before{content:"\e026"}.glyphicon-upload:before{content:"\e027"}.glyphicon-inbox:before{content:"\e028"}.glyphicon-play-circle:before{content:"\e029"}.glyphicon-repeat:before{content:"\e030"}.glyphicon-refresh:before{content:"\e031"}.glyphicon-list-alt:before{content:"\e032"}.glyphicon-lock:before{content:"\e033"}.glyphicon-flag:before{content:"\e034"}.glyphicon-headphones:before{content:"\e035"}.glyphicon-volume-off:before{content:"\e036"}.glyphicon-volume-down:before{content:"\e037"}.glyphicon-volume-up:before{content:"\e038"}.glyphicon-qrcode:before{content:"\e039"}.glyphicon-barcode:before{content:"\e040"}.glyphicon-tag:before{content:"\e041"}.glyphicon-tags:before{content:"\e042"}.glyphicon-book:before{content:"\e043"}.glyphicon-bookmark:before{content:"\e044"}.glyphicon-print:before{content:"\e045"}.glyphicon-camera:before{content:"\e046"}.glyphicon-font:before{content:"\e047"}.glyphicon-bold:before{content:"\e048"}.glyphicon-italic:before{content:"\e049"}.glyphicon-text-height:before{content:"\e050"}.glyphicon-text-width:before{content:"\e051"}.glyphicon-align-left:before{content:"\e052"}.glyphicon-align-center:before{content:"\e053"}.glyphicon-align-right:before{content:"\e054"}.glyphicon-align-justify:before{content:"\e055"}.glyphicon-list:before{content:"\e056"}.glyphicon-indent-left:before{content:"\e057"}.glyphicon-indent-right:before{content:"\e058"}.glyphicon-facetime-video:before{content:"\e059"}.glyphicon-picture:before{content:"\e060"}.glyphicon-map-marker:before{content:"\e062"}.glyphicon-adjust:before{content:"\e063"}.glyphicon-tint:before{content:"\e064"}.glyphicon-edit:before{content:"\e065"}.glyphicon-share:before{content:"\e066"}.glyphicon-check:before{content:"\e067"}.glyphicon-move:before{content:"\e068"}.glyphicon-step-backward:before{content:"\e069"}.glyphicon-fast-backward:before{content:"\e070"}.glyphicon-backward:before{content:"\e071"}.glyphicon-play:before{content:"\e072"}.glyphicon-pause:before{content:"\e073"}.glyphicon-stop:before{content:"\e074"}.glyphicon-forward:before{content:"\e075"}.glyphicon-fast-forward:before{content:"\e076"}.glyphicon-step-forward:before{content:"\e077"}.glyphicon-eject:before{content:"\e078"}.glyphicon-chevron-left:before{content:"\e079"}.glyphicon-chevron-right:before{content:"\e080"}.glyphicon-plus-sign:before{content:"\e081"}.glyphicon-minus-sign:before{content:"\e082"}.glyphicon-remove-sign:before{content:"\e083"}.glyphicon-ok-sign:before{content:"\e084"}.glyphicon-question-sign:before{content:"\e085"}.glyphicon-info-sign:before{content:"\e086"}.glyphicon-screenshot:before{content:"\e087"}.glyphicon-remove-circle:before{content:"\e088"}.glyphicon-ok-circle:before{content:"\e089"}.glyphicon-ban-circle:before{content:"\e090"}.glyphicon-arrow-left:before{content:"\e091"}.glyphicon-arrow-right:before{content:"\e092"}.glyphicon-arrow-up:before{content:"\e093"}.glyphicon-arrow-down:before{content:"\e094"}.glyphicon-share-alt:before{content:"\e095"}.glyphicon-resize-full:before{content:"\e096"}.glyphicon-resize-small:before{content:"\e097"}.glyphicon-exclamation-sign:before{content:"\e101"}.glyphicon-gift:before{content:"\e102"}.glyphicon-leaf:before{content:"\e103"}.glyphicon-fire:before{content:"\e104"}.glyphicon-eye-open:before{content:"\e105"}.glyphicon-eye-close:before{content:"\e106"}.glyphicon-warning-sign:before{content:"\e107"}.glyphicon-plane:before{content:"\e108"}.glyphicon-calendar:before{content:"\e109"}.glyphicon-random:before{content:"\e110"}.glyphicon-comment:before{content:"\e111"}.glyphicon-magnet:before{content:"\e112"}.glyphicon-chevron-up:before{content:"\e113"}.glyphicon-chevron-down:before{content:"\e114"}.glyphicon-retweet:before{content:"\e115"}.glyphicon-shopping-cart:before{content:"\e116"}.glyphicon-folder-close:before{content:"\e117"}.glyphicon-folder-open:before{content:"\e118"}.glyphicon-resize-vertical:before{content:"\e119"}.glyphicon-resize-horizontal:before{content:"\e120"}.glyphicon-hdd:before{content:"\e121"}.glyphicon-bullhorn:before{content:"\e122"}.glyphicon-bell:before{content:"\e123"}.glyphicon-certificate:before{content:"\e124"}.glyphicon-thumbs-up:before{content:"\e125"}.glyphicon-thumbs-down:before{content:"\e126"}.glyphicon-hand-right:before{content:"\e127"}.glyphicon-hand-left:before{content:"\e128"}.glyphicon-hand-up:before{content:"\e129"}.glyphicon-hand-down:before{content:"\e130"}.glyphicon-circle-arrow-right:before{content:"\e131"}.glyphicon-circle-arrow-left:before{content:"\e132"}.glyphicon-circle-arrow-up:before{content:"\e133"}.glyphicon-circle-arrow-down:before{content:"\e134"}.glyphicon-globe:before{content:"\e135"}.glyphicon-wrench:before{content:"\e136"}.glyphicon-tasks:before{content:"\e137"}.glyphicon-filter:before{content:"\e138"}.glyphicon-briefcase:before{content:"\e139"}.glyphicon-fullscreen:before{content:"\e140"}.glyphicon-dashboard:before{content:"\e141"}.glyphicon-paperclip:before{content:"\e142"}.glyphicon-heart-empty:before{content:"\e143"}.glyphicon-link:before{content:"\e144"}.glyphicon-phone:before{content:"\e145"}.glyphicon-pushpin:before{content:"\e146"}.glyphicon-usd:before{content:"\e148"}.glyphicon-gbp:before{content:"\e149"}.glyphicon-sort:before{content:"\e150"}.glyphicon-sort-by-alphabet:before{content:"\e151"}.glyphicon-sort-by-alphabet-alt:before{content:"\e152"}.glyphicon-sort-by-order:before{content:"\e153"}.glyphicon-sort-by-order-alt:before{content:"\e154"}.glyphicon-sort-by-attributes:before{content:"\e155"}.glyphicon-sort-by-attributes-alt:before{content:"\e156"}.glyphicon-unchecked:before{content:"\e157"}.glyphicon-expand:before{content:"\e158"}.glyphicon-collapse-down:before{content:"\e159"}.glyphicon-collapse-up:before{content:"\e160"}.glyphicon-log-in:before{content:"\e161"}.glyphicon-flash:before{content:"\e162"}.glyphicon-log-out:before{content:"\e163"}.glyphicon-new-window:before{content:"\e164"}.glyphicon-record:before{content:"\e165"}.glyphicon-save:before{content:"\e166"}.glyphicon-open:before{content:"\e167"}.glyphicon-saved:before{content:"\e168"}.glyphicon-import:before{content:"\e169"}.glyphicon-export:before{content:"\e170"}.glyphicon-send:before{content:"\e171"}.glyphicon-floppy-disk:before{content:"\e172"}.glyphicon-floppy-saved:before{content:"\e173"}.glyphicon-floppy-remove:before{content:"\e174"}.glyphicon-floppy-save:before{content:"\e175"}.glyphicon-floppy-open:before{content:"\e176"}.glyphicon-credit-card:before{content:"\e177"}.glyphicon-transfer:before{content:"\e178"}.glyphicon-cutlery:before{content:"\e179"}.glyphicon-header:before{content:"\e180"}.glyphicon-compressed:before{content:"\e181"}.glyphicon-earphone:before{content:"\e182"}.glyphicon-phone-alt:before{content:"\e183"}.glyphicon-tower:before{content:"\e184"}.glyphicon-stats:before{content:"\e185"}.glyphicon-sd-video:before{content:"\e186"}.glyphicon-hd-video:before{content:"\e187"}.glyphicon-subtitles:before{content:"\e188"}.glyphicon-sound-stereo:before{content:"\e189"}.glyphicon-sound-dolby:before{content:"\e190"}.glyphicon-sound-5-1:before{content:"\e191"}.glyphicon-sound-6-1:before{content:"\e192"}.glyphicon-sound-7-1:before{content:"\e193"}.glyphicon-copyright-mark:before{content:"\e194"}.glyphicon-registration-mark:before{content:"\e195"}.glyphicon-cloud-download:before{content:"\e197"}.glyphicon-cloud-upload:before{content:"\e198"}.glyphicon-tree-conifer:before{content:"\e199"}.glyphicon-tree-deciduous:before{content:"\e200"}.glyphicon-cd:before{content:"\e201"}.glyphicon-save-file:before{content:"\e202"}.glyphicon-open-file:before{content:"\e203"}.glyphicon-level-up:before{content:"\e204"}.glyphicon-copy:before{content:"\e205"}.glyphicon-paste:before{content:"\e206"}.glyphicon-alert:before{content:"\e209"}.glyphicon-equalizer:before{content:"\e210"}.glyphicon-king:before{content:"\e211"}.glyphicon-queen:before{content:"\e212"}.glyphicon-pawn:before{content:"\e213"}.glyphicon-bishop:before{content:"\e214"}.glyphicon-knight:before{content:"\e215"}.glyphicon-baby-formula:before{content:"\e216"}.glyphicon-tent:before{content:"\26fa"}.glyphicon-blackboard:before{content:"\e218"}.glyphicon-bed:before{content:"\e219"}.glyphicon-apple:before{content:"\f8ff"}.glyphicon-erase:before{content:"\e221"}.glyphicon-hourglass:before{content:"\231b"}.glyphicon-lamp:before{content:"\e223"}.glyphicon-duplicate:before{content:"\e224"}.glyphicon-piggy-bank:before{content:"\e225"}.glyphicon-scissors:before{content:"\e226"}.glyphicon-bitcoin:before{content:"\e227"}.glyphicon-btc:before{content:"\e227"}.glyphicon-xbt:before{content:"\e227"}.glyphicon-yen:before{content:"\00a5"}.glyphicon-jpy:before{content:"\00a5"}.glyphicon-ruble:before{content:"\20bd"}.glyphicon-rub:before{content:"\20bd"}.glyphicon-scale:before{content:"\e230"}.glyphicon-ice-lolly:before{content:"\e231"}.glyphicon-ice-lolly-tasted:before{content:"\e232"}.glyphicon-education:before{content:"\e233"}.glyphicon-option-horizontal:before{content:"\e234"}.glyphicon-option-vertical:before{content:"\e235"}.glyphicon-menu-hamburger:before{content:"\e236"}.glyphicon-modal-window:before{content:"\e237"}.glyphicon-oil:before{content:"\e238"}.glyphicon-grain:before{content:"\e239"}.glyphicon-sunglasses:before{content:"\e240"}.glyphicon-text-size:before{content:"\e241"}.glyphicon-text-color:before{content:"\e242"}.glyphicon-text-background:before{content:"\e243"}.glyphicon-object-align-top:before{content:"\e244"}.glyphicon-object-align-bottom:before{content:"\e245"}.glyphicon-object-align-horizontal:before{content:"\e246"}.glyphicon-object-align-left:before{content:"\e247"}.glyphicon-object-align-vertical:before{content:"\e248"}.glyphicon-object-align-right:before{content:"\e249"}.glyphicon-triangle-right:before{content:"\e250"}.glyphicon-triangle-left:before{content:"\e251"}.glyphicon-triangle-bottom:before{content:"\e252"}.glyphicon-triangle-top:before{content:"\e253"}.glyphicon-console:before{content:"\e254"}.glyphicon-superscript:before{content:"\e255"}.glyphicon-subscript:before{content:"\e256"}.glyphicon-menu-left:before{content:"\e257"}.glyphicon-menu-right:before{content:"\e258"}.glyphicon-menu-down:before{content:"\e259"}.glyphicon-menu-up:before{content:"\e260"}*{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}:after,:before{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}html{font-size:10px;-webkit-tap-highlight-color:rgba(0,0,0,0)}body{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;line-height:1.42857143;color:#333;background-color:#fff}button,input,select,textarea{font-family:inherit;font-size:inherit;line-height:inherit}a{color:#337ab7;text-decoration:none}a:focus,a:hover{color:#23527c;text-decoration:underline}a:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}figure{margin:0}img{vertical-align:middle}.carousel-inner>.item>a>img,.carousel-inner>.item>img,.img-responsive,.thumbnail a>img,.thumbnail>img{display:block;max-width:100%;height:auto}.img-rounded{border-radius:6px}.img-thumbnail{display:inline-block;max-width:100%;height:auto;padding:4px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}.img-circle{border-radius:50%}hr{margin-top:20px;margin-bottom:20px;border:0;border-top:1px solid #eee}.sr-only{position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);border:0}.sr-only-focusable:active,.sr-only-focusable:focus{position:static;width:auto;height:auto;margin:0;overflow:visible;clip:auto}[role=button]{cursor:pointer}.h1,.h2,.h3,.h4,.h5,.h6,h1,h2,h3,h4,h5,h6{font-family:inherit;font-weight:500;line-height:1.1;color:inherit}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-weight:400;line-height:1;color:#777}.h1,.h2,.h3,h1,h2,h3{margin-top:20px;margin-bottom:10px}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small{font-size:65%}.h4,.h5,.h6,h4,h5,h6{margin-top:10px;margin-bottom:10px}.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-size:75%}.h1,h1{font-size:36px}.h2,h2{font-size:30px}.h3,h3{font-size:24px}.h4,h4{font-size:18px}.h5,h5{font-size:14px}.h6,h6{font-size:12px}p{margin:0 0 10px}.lead{margin-bottom:20px;font-size:16px;font-weight:300;line-height:1.4}@media (min-width:768px){.lead{font-size:21px}}.small,small{font-size:85%}.mark,mark{padding:.2em;background-color:#fcf8e3}.text-left{text-align:left}.text-right{text-align:right}.text-center{text-align:center}.text-justify{text-align:justify}.text-nowrap{white-space:nowrap}.text-lowercase{text-transform:lowercase}.text-uppercase{text-transform:uppercase}.text-capitalize{text-transform:capitalize}.text-muted{color:#777}.text-primary{color:#337ab7}a.text-primary:focus,a.text-primary:hover{color:#286090}.text-success{color:#3c763d}a.text-success:focus,a.text-success:hover{color:#2b542c}.text-info{color:#31708f}a.text-info:focus,a.text-info:hover{color:#245269}.text-warning{color:#8a6d3b}a.text-warning:focus,a.text-warning:hover{color:#66512c}.text-danger{color:#a94442}a.text-danger:focus,a.text-danger:hover{color:#843534}.bg-primary{color:#fff;background-color:#337ab7}a.bg-primary:focus,a.bg-primary:hover{background-color:#286090}.bg-success{background-color:#dff0d8}a.bg-success:focus,a.bg-success:hover{background-color:#c1e2b3}.bg-info{background-color:#d9edf7}a.bg-info:focus,a.bg-info:hover{background-color:#afd9ee}.bg-warning{background-color:#fcf8e3}a.bg-warning:focus,a.bg-warning:hover{background-color:#f7ecb5}.bg-danger{background-color:#f2dede}a.bg-danger:focus,a.bg-danger:hover{background-color:#e4b9b9}.page-header{padding-bottom:9px;margin:40px 0 20px;border-bottom:1px solid #eee}ol,ul{margin-top:0;margin-bottom:10px}ol ol,ol ul,ul ol,ul ul{margin-bottom:0}.list-unstyled{padding-left:0;list-style:none}.list-inline{padding-left:0;margin-left:-5px;list-style:none}.list-inline>li{display:inline-block;padding-right:5px;padding-left:5px}dl{margin-top:0;margin-bottom:20px}dd,dt{line-height:1.42857143}dt{font-weight:700}dd{margin-left:0}@media (min-width:768px){.dl-horizontal dt{float:left;width:160px;overflow:hidden;clear:left;text-align:right;text-overflow:ellipsis;white-space:nowrap}.dl-horizontal dd{margin-left:180px}}abbr[data-original-title],abbr[title]{cursor:help;border-bottom:1px dotted #777}.initialism{font-size:90%;text-transform:uppercase}blockquote{padding:10px 20px;margin:0 0 20px;font-size:17.5px;border-left:5px solid #eee}blockquote ol:last-child,blockquote p:last-child,blockquote ul:last-child{margin-bottom:0}blockquote .small,blockquote footer,blockquote small{display:block;font-size:80%;line-height:1.42857143;color:#777}blockquote .small:before,blockquote footer:before,blockquote small:before{content:'\2014 \00A0'}.blockquote-reverse,blockquote.pull-right{padding-right:15px;padding-left:0;text-align:right;border-right:5px solid #eee;border-left:0}.blockquote-reverse .small:before,.blockquote-reverse footer:before,.blockquote-reverse small:before,blockquote.pull-right .small:before,blockquote.pull-right footer:before,blockquote.pull-right small:before{content:''}.blockquote-reverse .small:after,.blockquote-reverse footer:after,.blockquote-reverse small:after,blockquote.pull-right .small:after,blockquote.pull-right footer:after,blockquote.pull-right small:after{content:'\00A0 \2014'}address{margin-bottom:20px;font-style:normal;line-height:1.42857143}code,kbd,pre,samp{font-family:monospace}code{padding:2px 4px;font-size:90%;color:#c7254e;background-color:#f9f2f4;border-radius:4px}kbd{padding:2px 4px;font-size:90%;color:#fff;background-color:#333;border-radius:3px;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.25);box-shadow:inset 0 -1px 0 rgba(0,0,0,.25)}kbd kbd{padding:0;font-size:100%;font-weight:700;-webkit-box-shadow:none;box-shadow:none}pre{display:block;padding:9.5px;margin:0 0 10px;font-size:13px;line-height:1.42857143;color:#333;word-break:break-all;word-wrap:break-word;background-color:#f5f5f5;border:1px solid #ccc;border-radius:4px}pre code{padding:0;font-size:inherit;color:inherit;white-space:pre-wrap;background-color:transparent;border-radius:0}.pre-scrollable{max-height:340px;overflow-y:scroll}.container{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}@media (min-width:768px){.container{width:750px}}@media (min-width:992px){.container{width:970px}}@media (min-width:1200px){.container{width:1170px}}.container-fluid{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}.row{margin-right:-15px;margin-left:-15px}.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9,.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9,.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9,.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{position:relative;min-height:1px;padding-right:15px;padding-left:15px}.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{float:left}.col-xs-12{width:100%}.col-xs-11{width:91.66666667%}.col-xs-10{width:83.33333333%}.col-xs-9{width:75%}.col-xs-8{width:66.66666667%}.col-xs-7{width:58.33333333%}.col-xs-6{width:50%}.col-xs-5{width:41.66666667%}.col-xs-4{width:33.33333333%}.col-xs-3{width:25%}.col-xs-2{width:16.66666667%}.col-xs-1{width:8.33333333%}.col-xs-pull-12{right:100%}.col-xs-pull-11{right:91.66666667%}.col-xs-pull-10{right:83.33333333%}.col-xs-pull-9{right:75%}.col-xs-pull-8{right:66.66666667%}.col-xs-pull-7{right:58.33333333%}.col-xs-pull-6{right:50%}.col-xs-pull-5{right:41.66666667%}.col-xs-pull-4{right:33.33333333%}.col-xs-pull-3{right:25%}.col-xs-pull-2{right:16.66666667%}.col-xs-pull-1{right:8.33333333%}.col-xs-pull-0{right:auto}.col-xs-push-12{left:100%}.col-xs-push-11{left:91.66666667%}.col-xs-push-10{left:83.33333333%}.col-xs-push-9{left:75%}.col-xs-push-8{left:66.66666667%}.col-xs-push-7{left:58.33333333%}.col-xs-push-6{left:50%}.col-xs-push-5{left:41.66666667%}.col-xs-push-4{left:33.33333333%}.col-xs-push-3{left:25%}.col-xs-push-2{left:16.66666667%}.col-xs-push-1{left:8.33333333%}.col-xs-push-0{left:auto}.col-xs-offset-12{margin-left:100%}.col-xs-offset-11{margin-left:91.66666667%}.col-xs-offset-10{margin-left:83.33333333%}.col-xs-offset-9{margin-left:75%}.col-xs-offset-8{margin-left:66.66666667%}.col-xs-offset-7{margin-left:58.33333333%}.col-xs-offset-6{margin-left:50%}.col-xs-offset-5{margin-left:41.66666667%}.col-xs-offset-4{margin-left:33.33333333%}.col-xs-offset-3{margin-left:25%}.col-xs-offset-2{margin-left:16.66666667%}.col-xs-offset-1{margin-left:8.33333333%}.col-xs-offset-0{margin-left:0}@media (min-width:768px){.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9{float:left}.col-sm-12{width:100%}.col-sm-11{width:91.66666667%}.col-sm-10{width:83.33333333%}.col-sm-9{width:75%}.col-sm-8{width:66.66666667%}.col-sm-7{width:58.33333333%}.col-sm-6{width:50%}.col-sm-5{width:41.66666667%}.col-sm-4{width:33.33333333%}.col-sm-3{width:25%}.col-sm-2{width:16.66666667%}.col-sm-1{width:8.33333333%}.col-sm-pull-12{right:100%}.col-sm-pull-11{right:91.66666667%}.col-sm-pull-10{right:83.33333333%}.col-sm-pull-9{right:75%}.col-sm-pull-8{right:66.66666667%}.col-sm-pull-7{right:58.33333333%}.col-sm-pull-6{right:50%}.col-sm-pull-5{right:41.66666667%}.col-sm-pull-4{right:33.33333333%}.col-sm-pull-3{right:25%}.col-sm-pull-2{right:16.66666667%}.col-sm-pull-1{right:8.33333333%}.col-sm-pull-0{right:auto}.col-sm-push-12{left:100%}.col-sm-push-11{left:91.66666667%}.col-sm-push-10{left:83.33333333%}.col-sm-push-9{left:75%}.col-sm-push-8{left:66.66666667%}.col-sm-push-7{left:58.33333333%}.col-sm-push-6{left:50%}.col-sm-push-5{left:41.66666667%}.col-sm-push-4{left:33.33333333%}.col-sm-push-3{left:25%}.col-sm-push-2{left:16.66666667%}.col-sm-push-1{left:8.33333333%}.col-sm-push-0{left:auto}.col-sm-offset-12{margin-left:100%}.col-sm-offset-11{margin-left:91.66666667%}.col-sm-offset-10{margin-left:83.33333333%}.col-sm-offset-9{margin-left:75%}.col-sm-offset-8{margin-left:66.66666667%}.col-sm-offset-7{margin-left:58.33333333%}.col-sm-offset-6{margin-left:50%}.col-sm-offset-5{margin-left:41.66666667%}.col-sm-offset-4{margin-left:33.33333333%}.col-sm-offset-3{margin-left:25%}.col-sm-offset-2{margin-left:16.66666667%}.col-sm-offset-1{margin-left:8.33333333%}.col-sm-offset-0{margin-left:0}}@media (min-width:992px){.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9{float:left}.col-md-12{width:100%}.col-md-11{width:91.66666667%}.col-md-10{width:83.33333333%}.col-md-9{width:75%}.col-md-8{width:66.66666667%}.col-md-7{width:58.33333333%}.col-md-6{width:50%}.col-md-5{width:41.66666667%}.col-md-4{width:33.33333333%}.col-md-3{width:25%}.col-md-2{width:16.66666667%}.col-md-1{width:8.33333333%}.col-md-pull-12{right:100%}.col-md-pull-11{right:91.66666667%}.col-md-pull-10{right:83.33333333%}.col-md-pull-9{right:75%}.col-md-pull-8{right:66.66666667%}.col-md-pull-7{right:58.33333333%}.col-md-pull-6{right:50%}.col-md-pull-5{right:41.66666667%}.col-md-pull-4{right:33.33333333%}.col-md-pull-3{right:25%}.col-md-pull-2{right:16.66666667%}.col-md-pull-1{right:8.33333333%}.col-md-pull-0{right:auto}.col-md-push-12{left:100%}.col-md-push-11{left:91.66666667%}.col-md-push-10{left:83.33333333%}.col-md-push-9{left:75%}.col-md-push-8{left:66.66666667%}.col-md-push-7{left:58.33333333%}.col-md-push-6{left:50%}.col-md-push-5{left:41.66666667%}.col-md-push-4{left:33.33333333%}.col-md-push-3{left:25%}.col-md-push-2{left:16.66666667%}.col-md-push-1{left:8.33333333%}.col-md-push-0{left:auto}.col-md-offset-12{margin-left:100%}.col-md-offset-11{margin-left:91.66666667%}.col-md-offset-10{margin-left:83.33333333%}.col-md-offset-9{margin-left:75%}.col-md-offset-8{margin-left:66.66666667%}.col-md-offset-7{margin-left:58.33333333%}.col-md-offset-6{margin-left:50%}.col-md-offset-5{margin-left:41.66666667%}.col-md-offset-4{margin-left:33.33333333%}.col-md-offset-3{margin-left:25%}.col-md-offset-2{margin-left:16.66666667%}.col-md-offset-1{margin-left:8.33333333%}.col-md-offset-0{margin-left:0}}@media (min-width:1200px){.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9{float:left}.col-lg-12{width:100%}.col-lg-11{width:91.66666667%}.col-lg-10{width:83.33333333%}.col-lg-9{width:75%}.col-lg-8{width:66.66666667%}.col-lg-7{width:58.33333333%}.col-lg-6{width:50%}.col-lg-5{width:41.66666667%}.col-lg-4{width:33.33333333%}.col-lg-3{width:25%}.col-lg-2{width:16.66666667%}.col-lg-1{width:8.33333333%}.col-lg-pull-12{right:100%}.col-lg-pull-11{right:91.66666667%}.col-lg-pull-10{right:83.33333333%}.col-lg-pull-9{right:75%}.col-lg-pull-8{right:66.66666667%}.col-lg-pull-7{right:58.33333333%}.col-lg-pull-6{right:50%}.col-lg-pull-5{right:41.66666667%}.col-lg-pull-4{right:33.33333333%}.col-lg-pull-3{right:25%}.col-lg-pull-2{right:16.66666667%}.col-lg-pull-1{right:8.33333333%}.col-lg-pull-0{right:auto}.col-lg-push-12{left:100%}.col-lg-push-11{left:91.66666667%}.col-lg-push-10{left:83.33333333%}.col-lg-push-9{left:75%}.col-lg-push-8{left:66.66666667%}.col-lg-push-7{left:58.33333333%}.col-lg-push-6{left:50%}.col-lg-push-5{left:41.66666667%}.col-lg-push-4{left:33.33333333%}.col-lg-push-3{left:25%}.col-lg-push-2{left:16.66666667%}.col-lg-push-1{left:8.33333333%}.col-lg-push-0{left:auto}.col-lg-offset-12{margin-left:100%}.col-lg-offset-11{margin-left:91.66666667%}.col-lg-offset-10{margin-left:83.33333333%}.col-lg-offset-9{margin-left:75%}.col-lg-offset-8{margin-left:66.66666667%}.col-lg-offset-7{margin-left:58.33333333%}.col-lg-offset-6{margin-left:50%}.col-lg-offset-5{margin-left:41.66666667%}.col-lg-offset-4{margin-left:33.33333333%}.col-lg-offset-3{margin-left:25%}.col-lg-offset-2{margin-left:16.66666667%}.col-lg-offset-1{margin-left:8.33333333%}.col-lg-offset-0{margin-left:0}}table{background-color:transparent}caption{padding-top:8px;padding-bottom:8px;color:#777;text-align:left}th{}.table{width:100%;max-width:100%;margin-bottom:20px}.table>tbody>tr>td,.table>tbody>tr>th,.table>tfoot>tr>td,.table>tfoot>tr>th,.table>thead>tr>td,.table>thead>tr>th{padding:8px;line-height:1.42857143;vertical-align:top;border-top:1px solid #ddd}.table>thead>tr>th{vertical-align:bottom;border-bottom:2px solid #ddd}.table>caption+thead>tr:first-child>td,.table>caption+thead>tr:first-child>th,.table>colgroup+thead>tr:first-child>td,.table>colgroup+thead>tr:first-child>th,.table>thead:first-child>tr:first-child>td,.table>thead:first-child>tr:first-child>th{border-top:0}.table>tbody+tbody{border-top:2px solid #ddd}.table .table{background-color:#fff}.table-condensed>tbody>tr>td,.table-condensed>tbody>tr>th,.table-condensed>tfoot>tr>td,.table-condensed>tfoot>tr>th,.table-condensed>thead>tr>td,.table-condensed>thead>tr>th{padding:5px}.table-bordered{border:1px solid #ddd}.table-bordered>tbody>tr>td,.table-bordered>tbody>tr>th,.table-bordered>tfoot>tr>td,.table-bordered>tfoot>tr>th,.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border:1px solid #ddd}.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border-bottom-width:2px}.table-striped>tbody>tr:nth-of-type(odd){background-color:#f9f9f9}.table-hover>tbody>tr:hover{background-color:#f5f5f5}table col[class*=col-]{position:static;display:table-column;float:none}table td[class*=col-],table th[class*=col-]{position:static;display:table-cell;float:none}.table>tbody>tr.active>td,.table>tbody>tr.active>th,.table>tbody>tr>td.active,.table>tbody>tr>th.active,.table>tfoot>tr.active>td,.table>tfoot>tr.active>th,.table>tfoot>tr>td.active,.table>tfoot>tr>th.active,.table>thead>tr.active>td,.table>thead>tr.active>th,.table>thead>tr>td.active,.table>thead>tr>th.active{background-color:#f5f5f5}.table-hover>tbody>tr.active:hover>td,.table-hover>tbody>tr.active:hover>th,.table-hover>tbody>tr:hover>.active,.table-hover>tbody>tr>td.active:hover,.table-hover>tbody>tr>th.active:hover{background-color:#e8e8e8}.table>tbody>tr.success>td,.table>tbody>tr.success>th,.table>tbody>tr>td.success,.table>tbody>tr>th.success,.table>tfoot>tr.success>td,.table>tfoot>tr.success>th,.table>tfoot>tr>td.success,.table>tfoot>tr>th.success,.table>thead>tr.success>td,.table>thead>tr.success>th,.table>thead>tr>td.success,.table>thead>tr>th.success{background-color:#dff0d8}.table-hover>tbody>tr.success:hover>td,.table-hover>tbody>tr.success:hover>th,.table-hover>tbody>tr:hover>.success,.table-hover>tbody>tr>td.success:hover,.table-hover>tbody>tr>th.success:hover{background-color:#d0e9c6}.table>tbody>tr.info>td,.table>tbody>tr.info>th,.table>tbody>tr>td.info,.table>tbody>tr>th.info,.table>tfoot>tr.info>td,.table>tfoot>tr.info>th,.table>tfoot>tr>td.info,.table>tfoot>tr>th.info,.table>thead>tr.info>td,.table>thead>tr.info>th,.table>thead>tr>td.info,.table>thead>tr>th.info{background-color:#d9edf7}.table-hover>tbody>tr.info:hover>td,.table-hover>tbody>tr.info:hover>th,.table-hover>tbody>tr:hover>.info,.table-hover>tbody>tr>td.info:hover,.table-hover>tbody>tr>th.info:hover{background-color:#c4e3f3}.table>tbody>tr.warning>td,.table>tbody>tr.warning>th,.table>tbody>tr>td.warning,.table>tbody>tr>th.warning,.table>tfoot>tr.warning>td,.table>tfoot>tr.warning>th,.table>tfoot>tr>td.warning,.table>tfoot>tr>th.warning,.table>thead>tr.warning>td,.table>thead>tr.warning>th,.table>thead>tr>td.warning,.table>thead>tr>th.warning{background-color:#fcf8e3}.table-hover>tbody>tr.warning:hover>td,.table-hover>tbody>tr.warning:hover>th,.table-hover>tbody>tr:hover>.warning,.table-hover>tbody>tr>td.warning:hover,.table-hover>tbody>tr>th.warning:hover{background-color:#faf2cc}.table>tbody>tr.danger>td,.table>tbody>tr.danger>th,.table>tbody>tr>td.danger,.table>tbody>tr>th.danger,.table>tfoot>tr.danger>td,.table>tfoot>tr.danger>th,.table>tfoot>tr>td.danger,.table>tfoot>tr>th.danger,.table>thead>tr.danger>td,.table>thead>tr.danger>th,.table>thead>tr>td.danger,.table>thead>tr>th.danger{background-color:#f2dede}.table-hover>tbody>tr.danger:hover>td,.table-hover>tbody>tr.danger:hover>th,.table-hover>tbody>tr:hover>.danger,.table-hover>tbody>tr>td.danger:hover,.table-hover>tbody>tr>th.danger:hover{background-color:#ebcccc}.table-responsive{min-height:.01%;overflow-x:auto}@media screen and (max-width:767px){.table-responsive{width:100%;margin-bottom:15px;overflow-y:hidden;-ms-overflow-style:-ms-autohiding-scrollbar;border:1px solid #ddd}.table-responsive>.table{margin-bottom:0}.table-responsive>.table>tbody>tr>td,.table-responsive>.table>tbody>tr>th,.table-responsive>.table>tfoot>tr>td,.table-responsive>.table>tfoot>tr>th,.table-responsive>.table>thead>tr>td,.table-responsive>.table>thead>tr>th{white-space:nowrap}.table-responsive>.table-bordered{border:0}.table-responsive>.table-bordered>tbody>tr>td:first-child,.table-responsive>.table-bordered>tbody>tr>th:first-child,.table-responsive>.table-bordered>tfoot>tr>td:first-child,.table-responsive>.table-bordered>tfoot>tr>th:first-child,.table-responsive>.table-bordered>thead>tr>td:first-child,.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.table-responsive>.table-bordered>tbody>tr>td:last-child,.table-responsive>.table-bordered>tbody>tr>th:last-child,.table-responsive>.table-bordered>tfoot>tr>td:last-child,.table-responsive>.table-bordered>tfoot>tr>th:last-child,.table-responsive>.table-bordered>thead>tr>td:last-child,.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.table-responsive>.table-bordered>tbody>tr:last-child>td,.table-responsive>.table-bordered>tbody>tr:last-child>th,.table-responsive>.table-bordered>tfoot>tr:last-child>td,.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}}fieldset{min-width:0;padding:0;margin:0;border:0}legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:21px;line-height:inherit;color:#333;border:0;border-bottom:1px solid #e5e5e5}label{display:inline-block;max-width:100%;margin-bottom:5px;font-weight:700}input[type=search]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}input[type=checkbox],input[type=radio]{margin:4px 0 0;margin-top:1px\9;line-height:normal}input[type=file]{display:block}input[type=range]{display:block;width:100%}select[multiple],select[size]{height:auto}input[type=file]:focus,input[type=checkbox]:focus,input[type=radio]:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}output{display:block;padding-top:7px;font-size:14px;line-height:1.42857143;color:#555}.form-control{display:block;width:100%;height:34px;padding:6px 12px;font-size:14px;line-height:1.42857143;color:#555;background-color:#fff;background-image:none;border:1px solid #ccc;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075);-webkit-transition:border-color ease-in-out .15s,-webkit-box-shadow ease-in-out .15s;-o-transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s;transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s}.form-control:focus{border-color:#66afe9;outline:0;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6);box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6)}.form-control::-moz-placeholder{color:#999;opacity:1}.form-control:-ms-input-placeholder{color:#999}.form-control::-webkit-input-placeholder{color:#999}.form-control[disabled],.form-control[readonly],fieldset[disabled] .form-control{background-color:#eee;opacity:1}.form-control[disabled],fieldset[disabled] .form-control{cursor:not-allowed}textarea.form-control{height:auto}input[type=search]{-webkit-appearance:none}@media screen and (-webkit-min-device-pixel-ratio:0){input[type=date].form-control,input[type=time].form-control,input[type=datetime-local].form-control,input[type=month].form-control{line-height:34px}.input-group-sm input[type=date],.input-group-sm input[type=time],.input-group-sm input[type=datetime-local],.input-group-sm input[type=month],input[type=date].input-sm,input[type=time].input-sm,input[type=datetime-local].input-sm,input[type=month].input-sm{line-height:30px}.input-group-lg input[type=date],.input-group-lg input[type=time],.input-group-lg input[type=datetime-local],.input-group-lg input[type=month],input[type=date].input-lg,input[type=time].input-lg,input[type=datetime-local].input-lg,input[type=month].input-lg{line-height:46px}}.form-group{margin-bottom:15px}.checkbox,.radio{position:relative;display:block;margin-top:10px;margin-bottom:10px}.checkbox label,.radio label{min-height:20px;padding-left:20px;margin-bottom:0;font-weight:400;cursor:pointer}.checkbox input[type=checkbox],.checkbox-inline input[type=checkbox],.radio input[type=radio],.radio-inline input[type=radio]{position:absolute;margin-top:4px\9;margin-left:-20px}.checkbox+.checkbox,.radio+.radio{margin-top:-5px}.checkbox-inline,.radio-inline{position:relative;display:inline-block;padding-left:20px;margin-bottom:0;font-weight:400;vertical-align:middle;cursor:pointer}.checkbox-inline+.checkbox-inline,.radio-inline+.radio-inline{margin-top:0;margin-left:10px}fieldset[disabled] input[type=checkbox],fieldset[disabled] input[type=radio],input[type=checkbox].disabled,input[type=checkbox][disabled],input[type=radio].disabled,input[type=radio][disabled]{cursor:not-allowed}.checkbox-inline.disabled,.radio-inline.disabled,fieldset[disabled] .checkbox-inline,fieldset[disabled] .radio-inline{cursor:not-allowed}.checkbox.disabled label,.radio.disabled label,fieldset[disabled] .checkbox label,fieldset[disabled] .radio label{cursor:not-allowed}.form-control-static{min-height:34px;padding-top:7px;padding-bottom:7px;margin-bottom:0}.form-control-static.input-lg,.form-control-static.input-sm{padding-right:0;padding-left:0}.input-sm{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-sm{height:30px;line-height:30px}select[multiple].input-sm,textarea.input-sm{height:auto}.form-group-sm .form-control{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.form-group-sm select.form-control{height:30px;line-height:30px}.form-group-sm select[multiple].form-control,.form-group-sm textarea.form-control{height:auto}.form-group-sm .form-control-static{height:30px;min-height:32px;padding:6px 10px;font-size:12px;line-height:1.5}.input-lg{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-lg{height:46px;line-height:46px}select[multiple].input-lg,textarea.input-lg{height:auto}.form-group-lg .form-control{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.form-group-lg select.form-control{height:46px;line-height:46px}.form-group-lg select[multiple].form-control,.form-group-lg textarea.form-control{height:auto}.form-group-lg .form-control-static{height:46px;min-height:38px;padding:11px 16px;font-size:18px;line-height:1.3333333}.has-feedback{position:relative}.has-feedback .form-control{padding-right:42.5px}.form-control-feedback{position:absolute;top:0;right:0;z-index:2;display:block;width:34px;height:34px;line-height:34px;text-align:center;pointer-events:none}.form-group-lg .form-control+.form-control-feedback,.input-group-lg+.form-control-feedback,.input-lg+.form-control-feedback{width:46px;height:46px;line-height:46px}.form-group-sm .form-control+.form-control-feedback,.input-group-sm+.form-control-feedback,.input-sm+.form-control-feedback{width:30px;height:30px;line-height:30px}.has-success .checkbox,.has-success .checkbox-inline,.has-success .control-label,.has-success .help-block,.has-success .radio,.has-success .radio-inline,.has-success.checkbox label,.has-success.checkbox-inline label,.has-success.radio label,.has-success.radio-inline label{color:#3c763d}.has-success .form-control{border-color:#3c763d;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-success .form-control:focus{border-color:#2b542c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168}.has-success .input-group-addon{color:#3c763d;background-color:#dff0d8;border-color:#3c763d}.has-success .form-control-feedback{color:#3c763d}.has-warning .checkbox,.has-warning .checkbox-inline,.has-warning .control-label,.has-warning .help-block,.has-warning .radio,.has-warning .radio-inline,.has-warning.checkbox label,.has-warning.checkbox-inline label,.has-warning.radio label,.has-warning.radio-inline label{color:#8a6d3b}.has-warning .form-control{border-color:#8a6d3b;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-warning .form-control:focus{border-color:#66512c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b}.has-warning .input-group-addon{color:#8a6d3b;background-color:#fcf8e3;border-color:#8a6d3b}.has-warning .form-control-feedback{color:#8a6d3b}.has-error .checkbox,.has-error .checkbox-inline,.has-error .control-label,.has-error .help-block,.has-error .radio,.has-error .radio-inline,.has-error.checkbox label,.has-error.checkbox-inline label,.has-error.radio label,.has-error.radio-inline label{color:#a94442}.has-error .form-control{border-color:#a94442;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-error .form-control:focus{border-color:#843534;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483}.has-error .input-group-addon{color:#a94442;background-color:#f2dede;border-color:#a94442}.has-error .form-control-feedback{color:#a94442}.has-feedback label~.form-control-feedback{top:25px}.has-feedback label.sr-only~.form-control-feedback{top:0}.help-block{display:block;margin-top:5px;margin-bottom:10px;color:#737373}@media (min-width:768px){.form-inline .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.form-inline .form-control{display:inline-block;width:auto;vertical-align:middle}.form-inline .form-control-static{display:inline-block}.form-inline .input-group{display:inline-table;vertical-align:middle}.form-inline .input-group .form-control,.form-inline .input-group .input-group-addon,.form-inline .input-group .input-group-btn{width:auto}.form-inline .input-group>.form-control{width:100%}.form-inline .control-label{margin-bottom:0;vertical-align:middle}.form-inline .checkbox,.form-inline .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.form-inline .checkbox label,.form-inline .radio label{padding-left:0}.form-inline .checkbox input[type=checkbox],.form-inline .radio input[type=radio]{position:relative;margin-left:0}.form-inline .has-feedback .form-control-feedback{top:0}}.form-horizontal .checkbox,.form-horizontal .checkbox-inline,.form-horizontal .radio,.form-horizontal .radio-inline{padding-top:7px;margin-top:0;margin-bottom:0}.form-horizontal .checkbox,.form-horizontal .radio{min-height:27px}.form-horizontal .form-group{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.form-horizontal .control-label{padding-top:7px;margin-bottom:0;text-align:right}}.form-horizontal .has-feedback .form-control-feedback{right:15px}@media (min-width:768px){.form-horizontal .form-group-lg .control-label{padding-top:14.33px;font-size:18px}}@media (min-width:768px){.form-horizontal .form-group-sm .control-label{padding-top:6px;font-size:12px}}.btn{display:inline-block;padding:6px 12px;margin-bottom:0;font-size:14px;font-weight:400;line-height:1.42857143;text-align:center;white-space:nowrap;vertical-align:middle;-ms-touch-action:manipulation;touch-action:manipulation;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;background-image:none;border:1px solid transparent;border-radius:4px}.btn.active.focus,.btn.active:focus,.btn.focus,.btn:active.focus,.btn:active:focus,.btn:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}.btn.focus,.btn:focus,.btn:hover{color:#333;text-decoration:none}.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn.disabled,.btn[disabled],fieldset[disabled] .btn{cursor:not-allowed;filter:alpha(opacity=65);-webkit-box-shadow:none;box-shadow:none;opacity:.65}a.btn.disabled,fieldset[disabled] a.btn{pointer-events:none}.btn-default{color:#333;background-color:#fff;border-color:#ccc}.btn-default.focus,.btn-default:focus{color:#333;background-color:#e6e6e6;border-color:#8c8c8c}.btn-default:hover{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active.focus,.btn-default.active:focus,.btn-default.active:hover,.btn-default:active.focus,.btn-default:active:focus,.btn-default:active:hover,.open>.dropdown-toggle.btn-default.focus,.open>.dropdown-toggle.btn-default:focus,.open>.dropdown-toggle.btn-default:hover{color:#333;background-color:#d4d4d4;border-color:#8c8c8c}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{background-image:none}.btn-default.disabled,.btn-default.disabled.active,.btn-default.disabled.focus,.btn-default.disabled:active,.btn-default.disabled:focus,.btn-default.disabled:hover,.btn-default[disabled],.btn-default[disabled].active,.btn-default[disabled].focus,.btn-default[disabled]:active,.btn-default[disabled]:focus,.btn-default[disabled]:hover,fieldset[disabled] .btn-default,fieldset[disabled] .btn-default.active,fieldset[disabled] .btn-default.focus,fieldset[disabled] .btn-default:active,fieldset[disabled] .btn-default:focus,fieldset[disabled] .btn-default:hover{background-color:#fff;border-color:#ccc}.btn-default .badge{color:#fff;background-color:#333}.btn-primary{color:#fff;background-color:#337ab7;border-color:#2e6da4}.btn-primary.focus,.btn-primary:focus{color:#fff;background-color:#286090;border-color:#122b40}.btn-primary:hover{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active.focus,.btn-primary.active:focus,.btn-primary.active:hover,.btn-primary:active.focus,.btn-primary:active:focus,.btn-primary:active:hover,.open>.dropdown-toggle.btn-primary.focus,.open>.dropdown-toggle.btn-primary:focus,.open>.dropdown-toggle.btn-primary:hover{color:#fff;background-color:#204d74;border-color:#122b40}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{background-image:none}.btn-primary.disabled,.btn-primary.disabled.active,.btn-primary.disabled.focus,.btn-primary.disabled:active,.btn-primary.disabled:focus,.btn-primary.disabled:hover,.btn-primary[disabled],.btn-primary[disabled].active,.btn-primary[disabled].focus,.btn-primary[disabled]:active,.btn-primary[disabled]:focus,.btn-primary[disabled]:hover,fieldset[disabled] .btn-primary,fieldset[disabled] .btn-primary.active,fieldset[disabled] .btn-primary.focus,fieldset[disabled] .btn-primary:active,fieldset[disabled] .btn-primary:focus,fieldset[disabled] .btn-primary:hover{background-color:#337ab7;border-color:#2e6da4}.btn-primary .badge{color:#337ab7;background-color:#fff}.btn-success{color:#fff;background-color:#5cb85c;border-color:#4cae4c}.btn-success.focus,.btn-success:focus{color:#fff;background-color:#449d44;border-color:#255625}.btn-success:hover{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active.focus,.btn-success.active:focus,.btn-success.active:hover,.btn-success:active.focus,.btn-success:active:focus,.btn-success:active:hover,.open>.dropdown-toggle.btn-success.focus,.open>.dropdown-toggle.btn-success:focus,.open>.dropdown-toggle.btn-success:hover{color:#fff;background-color:#398439;border-color:#255625}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{background-image:none}.btn-success.disabled,.btn-success.disabled.active,.btn-success.disabled.focus,.btn-success.disabled:active,.btn-success.disabled:focus,.btn-success.disabled:hover,.btn-success[disabled],.btn-success[disabled].active,.btn-success[disabled].focus,.btn-success[disabled]:active,.btn-success[disabled]:focus,.btn-success[disabled]:hover,fieldset[disabled] .btn-success,fieldset[disabled] .btn-success.active,fieldset[disabled] .btn-success.focus,fieldset[disabled] .btn-success:active,fieldset[disabled] .btn-success:focus,fieldset[disabled] .btn-success:hover{background-color:#5cb85c;border-color:#4cae4c}.btn-success .badge{color:#5cb85c;background-color:#fff}.btn-info{color:#fff;background-color:#5bc0de;border-color:#46b8da}.btn-info.focus,.btn-info:focus{color:#fff;background-color:#31b0d5;border-color:#1b6d85}.btn-info:hover{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active.focus,.btn-info.active:focus,.btn-info.active:hover,.btn-info:active.focus,.btn-info:active:focus,.btn-info:active:hover,.open>.dropdown-toggle.btn-info.focus,.open>.dropdown-toggle.btn-info:focus,.open>.dropdown-toggle.btn-info:hover{color:#fff;background-color:#269abc;border-color:#1b6d85}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{background-image:none}.btn-info.disabled,.btn-info.disabled.active,.btn-info.disabled.focus,.btn-info.disabled:active,.btn-info.disabled:focus,.btn-info.disabled:hover,.btn-info[disabled],.btn-info[disabled].active,.btn-info[disabled].focus,.btn-info[disabled]:active,.btn-info[disabled]:focus,.btn-info[disabled]:hover,fieldset[disabled] .btn-info,fieldset[disabled] .btn-info.active,fieldset[disabled] .btn-info.focus,fieldset[disabled] .btn-info:active,fieldset[disabled] .btn-info:focus,fieldset[disabled] .btn-info:hover{background-color:#5bc0de;border-color:#46b8da}.btn-info .badge{color:#5bc0de;background-color:#fff}.btn-warning{color:#fff;background-color:#f0ad4e;border-color:#eea236}.btn-warning.focus,.btn-warning:focus{color:#fff;background-color:#ec971f;border-color:#985f0d}.btn-warning:hover{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active.focus,.btn-warning.active:focus,.btn-warning.active:hover,.btn-warning:active.focus,.btn-warning:active:focus,.btn-warning:active:hover,.open>.dropdown-toggle.btn-warning.focus,.open>.dropdown-toggle.btn-warning:focus,.open>.dropdown-toggle.btn-warning:hover{color:#fff;background-color:#d58512;border-color:#985f0d}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{background-image:none}.btn-warning.disabled,.btn-warning.disabled.active,.btn-warning.disabled.focus,.btn-warning.disabled:active,.btn-warning.disabled:focus,.btn-warning.disabled:hover,.btn-warning[disabled],.btn-warning[disabled].active,.btn-warning[disabled].focus,.btn-warning[disabled]:active,.btn-warning[disabled]:focus,.btn-warning[disabled]:hover,fieldset[disabled] .btn-warning,fieldset[disabled] .btn-warning.active,fieldset[disabled] .btn-warning.focus,fieldset[disabled] .btn-warning:active,fieldset[disabled] .btn-warning:focus,fieldset[disabled] .btn-warning:hover{background-color:#f0ad4e;border-color:#eea236}.btn-warning .badge{color:#f0ad4e;background-color:#fff}.btn-danger{color:#fff;background-color:#d9534f;border-color:#d43f3a}.btn-danger.focus,.btn-danger:focus{color:#fff;background-color:#c9302c;border-color:#761c19}.btn-danger:hover{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active.focus,.btn-danger.active:focus,.btn-danger.active:hover,.btn-danger:active.focus,.btn-danger:active:focus,.btn-danger:active:hover,.open>.dropdown-toggle.btn-danger.focus,.open>.dropdown-toggle.btn-danger:focus,.open>.dropdown-toggle.btn-danger:hover{color:#fff;background-color:#ac2925;border-color:#761c19}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{background-image:none}.btn-danger.disabled,.btn-danger.disabled.active,.btn-danger.disabled.focus,.btn-danger.disabled:active,.btn-danger.disabled:focus,.btn-danger.disabled:hover,.btn-danger[disabled],.btn-danger[disabled].active,.btn-danger[disabled].focus,.btn-danger[disabled]:active,.btn-danger[disabled]:focus,.btn-danger[disabled]:hover,fieldset[disabled] .btn-danger,fieldset[disabled] .btn-danger.active,fieldset[disabled] .btn-danger.focus,fieldset[disabled] .btn-danger:active,fieldset[disabled] .btn-danger:focus,fieldset[disabled] .btn-danger:hover{background-color:#d9534f;border-color:#d43f3a}.btn-danger .badge{color:#d9534f;background-color:#fff}.btn-link{font-weight:400;color:#337ab7;border-radius:0}.btn-link,.btn-link.active,.btn-link:active,.btn-link[disabled],fieldset[disabled] .btn-link{background-color:transparent;-webkit-box-shadow:none;box-shadow:none}.btn-link,.btn-link:active,.btn-link:focus,.btn-link:hover{border-color:transparent}.btn-link:focus,.btn-link:hover{color:#23527c;text-decoration:underline;background-color:transparent}.btn-link[disabled]:focus,.btn-link[disabled]:hover,fieldset[disabled] .btn-link:focus,fieldset[disabled] .btn-link:hover{color:#777;text-decoration:none}.btn-group-lg>.btn,.btn-lg{padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.btn-group-sm>.btn,.btn-sm{padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.btn-group-xs>.btn,.btn-xs{padding:1px 5px;font-size:12px;line-height:1.5;border-radius:3px}.btn-block{display:block;width:100%}.btn-block+.btn-block{margin-top:5px}input[type=button].btn-block,input[type=reset].btn-block,input[type=submit].btn-block{width:100%}.fade{opacity:0;-webkit-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}.collapse{display:none}.collapse.in{display:block}tr.collapse.in{display:table-row}tbody.collapse.in{display:table-row-group}.collapsing{position:relative;height:0;overflow:hidden;-webkit-transition-timing-function:ease;-o-transition-timing-function:ease;transition-timing-function:ease;-webkit-transition-duration:.35s;-o-transition-duration:.35s;transition-duration:.35s;-webkit-transition-property:height,visibility;-o-transition-property:height,visibility;transition-property:height,visibility}.caret{display:inline-block;width:0;height:0;margin-left:2px;vertical-align:middle;border-top:4px dashed;border-top:4px solid\9;border-right:4px solid transparent;border-left:4px solid transparent}.dropdown,.dropup{position:relative}.dropdown-toggle:focus{outline:0}.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;font-size:14px;text-align:left;list-style:none;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.15);border-radius:4px;-webkit-box-shadow:0 6px 12px rgba(0,0,0,.175);box-shadow:0 6px 12px rgba(0,0,0,.175)}.dropdown-menu.pull-right{right:0;left:auto}.dropdown-menu .divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:400;line-height:1.42857143;color:#333;white-space:nowrap}.dropdown-menu>li>a:focus,.dropdown-menu>li>a:hover{color:#262626;text-decoration:none;background-color:#f5f5f5}.dropdown-menu>.active>a,.dropdown-menu>.active>a:focus,.dropdown-menu>.active>a:hover{color:#fff;text-decoration:none;background-color:#337ab7;outline:0}.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{color:#777}.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{text-decoration:none;cursor:not-allowed;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.open>.dropdown-menu{display:block}.open>a{outline:0}.dropdown-menu-right{right:0;left:auto}.dropdown-menu-left{right:auto;left:0}.dropdown-header{display:block;padding:3px 20px;font-size:12px;line-height:1.42857143;color:#777;white-space:nowrap}.dropdown-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:990}.pull-right>.dropdown-menu{right:0;left:auto}.dropup .caret,.navbar-fixed-bottom .dropdown .caret{content:"";border-top:0;border-bottom:4px dashed;border-bottom:4px solid\9}.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:2px}@media (min-width:768px){.navbar-right .dropdown-menu{right:0;left:auto}.navbar-right .dropdown-menu-left{right:auto;left:0}}.btn-group,.btn-group-vertical{position:relative;display:inline-block;vertical-align:middle}.btn-group-vertical>.btn,.btn-group>.btn{position:relative;float:left}.btn-group-vertical>.btn.active,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:hover,.btn-group>.btn.active,.btn-group>.btn:active,.btn-group>.btn:focus,.btn-group>.btn:hover{z-index:2}.btn-group .btn+.btn,.btn-group .btn+.btn-group,.btn-group .btn-group+.btn,.btn-group .btn-group+.btn-group{margin-left:-1px}.btn-toolbar{margin-left:-5px}.btn-toolbar .btn,.btn-toolbar .btn-group,.btn-toolbar .input-group{float:left}.btn-toolbar>.btn,.btn-toolbar>.btn-group,.btn-toolbar>.input-group{margin-left:5px}.btn-group>.btn:not(:first-child):not(:last-child):not(.dropdown-toggle){border-radius:0}.btn-group>.btn:first-child{margin-left:0}.btn-group>.btn:first-child:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:last-child:not(:first-child),.btn-group>.dropdown-toggle:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.btn-group>.btn-group{float:left}.btn-group>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-bottom-left-radius:0}.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}.btn-group>.btn+.dropdown-toggle{padding-right:8px;padding-left:8px}.btn-group>.btn-lg+.dropdown-toggle{padding-right:12px;padding-left:12px}.btn-group.open .dropdown-toggle{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-group.open .dropdown-toggle.btn-link{-webkit-box-shadow:none;box-shadow:none}.btn .caret{margin-left:0}.btn-lg .caret{border-width:5px 5px 0;border-bottom-width:0}.dropup .btn-lg .caret{border-width:0 5px 5px}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group,.btn-group-vertical>.btn-group>.btn{display:block;float:none;width:100%;max-width:100%}.btn-group-vertical>.btn-group>.btn{float:none}.btn-group-vertical>.btn+.btn,.btn-group-vertical>.btn+.btn-group,.btn-group-vertical>.btn-group+.btn,.btn-group-vertical>.btn-group+.btn-group{margin-top:-1px;margin-left:0}.btn-group-vertical>.btn:not(:first-child):not(:last-child){border-radius:0}.btn-group-vertical>.btn:first-child:not(:last-child){border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn:last-child:not(:first-child){border-top-left-radius:0;border-top-right-radius:0;border-bottom-left-radius:4px}.btn-group-vertical>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group-vertical>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group-vertical>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-top-right-radius:0}.btn-group-justified{display:table;width:100%;table-layout:fixed;border-collapse:separate}.btn-group-justified>.btn,.btn-group-justified>.btn-group{display:table-cell;float:none;width:1%}.btn-group-justified>.btn-group .btn{width:100%}.btn-group-justified>.btn-group .dropdown-menu{left:auto}[data-toggle=buttons]>.btn input[type=checkbox],[data-toggle=buttons]>.btn input[type=radio],[data-toggle=buttons]>.btn-group>.btn input[type=checkbox],[data-toggle=buttons]>.btn-group>.btn input[type=radio]{position:absolute;clip:rect(0,0,0,0);pointer-events:none}.input-group{position:relative;display:table;border-collapse:separate}.input-group[class*=col-]{float:none;padding-right:0;padding-left:0}.input-group .form-control{position:relative;z-index:2;float:left;width:100%;margin-bottom:0}.input-group-lg>.form-control,.input-group-lg>.input-group-addon,.input-group-lg>.input-group-btn>.btn{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-group-lg>.form-control,select.input-group-lg>.input-group-addon,select.input-group-lg>.input-group-btn>.btn{height:46px;line-height:46px}select[multiple].input-group-lg>.form-control,select[multiple].input-group-lg>.input-group-addon,select[multiple].input-group-lg>.input-group-btn>.btn,textarea.input-group-lg>.form-control,textarea.input-group-lg>.input-group-addon,textarea.input-group-lg>.input-group-btn>.btn{height:auto}.input-group-sm>.form-control,.input-group-sm>.input-group-addon,.input-group-sm>.input-group-btn>.btn{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-group-sm>.form-control,select.input-group-sm>.input-group-addon,select.input-group-sm>.input-group-btn>.btn{height:30px;line-height:30px}select[multiple].input-group-sm>.form-control,select[multiple].input-group-sm>.input-group-addon,select[multiple].input-group-sm>.input-group-btn>.btn,textarea.input-group-sm>.form-control,textarea.input-group-sm>.input-group-addon,textarea.input-group-sm>.input-group-btn>.btn{height:auto}.input-group .form-control,.input-group-addon,.input-group-btn{display:table-cell}.input-group .form-control:not(:first-child):not(:last-child),.input-group-addon:not(:first-child):not(:last-child),.input-group-btn:not(:first-child):not(:last-child){border-radius:0}.input-group-addon,.input-group-btn{width:1%;white-space:nowrap;vertical-align:middle}.input-group-addon{padding:6px 12px;font-size:14px;font-weight:400;line-height:1;color:#555;text-align:center;background-color:#eee;border:1px solid #ccc;border-radius:4px}.input-group-addon.input-sm{padding:5px 10px;font-size:12px;border-radius:3px}.input-group-addon.input-lg{padding:10px 16px;font-size:18px;border-radius:6px}.input-group-addon input[type=checkbox],.input-group-addon input[type=radio]{margin-top:0}.input-group .form-control:first-child,.input-group-addon:first-child,.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group>.btn,.input-group-btn:first-child>.dropdown-toggle,.input-group-btn:last-child>.btn-group:not(:last-child)>.btn,.input-group-btn:last-child>.btn:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.input-group-addon:first-child{border-right:0}.input-group .form-control:last-child,.input-group-addon:last-child,.input-group-btn:first-child>.btn-group:not(:first-child)>.btn,.input-group-btn:first-child>.btn:not(:first-child),.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group>.btn,.input-group-btn:last-child>.dropdown-toggle{border-top-left-radius:0;border-bottom-left-radius:0}.input-group-addon:last-child{border-left:0}.input-group-btn{position:relative;font-size:0;white-space:nowrap}.input-group-btn>.btn{position:relative}.input-group-btn>.btn+.btn{margin-left:-1px}.input-group-btn>.btn:active,.input-group-btn>.btn:focus,.input-group-btn>.btn:hover{z-index:2}.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group{margin-right:-1px}.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group{z-index:2;margin-left:-1px}.nav{padding-left:0;margin-bottom:0;list-style:none}.nav>li{position:relative;display:block}.nav>li>a{position:relative;display:block;padding:10px 15px}.nav>li>a:focus,.nav>li>a:hover{text-decoration:none;background-color:#eee}.nav>li.disabled>a{color:#777}.nav>li.disabled>a:focus,.nav>li.disabled>a:hover{color:#777;text-decoration:none;cursor:not-allowed;background-color:transparent}.nav .open>a,.nav .open>a:focus,.nav .open>a:hover{background-color:#eee;border-color:#337ab7}.nav .nav-divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.nav>li>a>img{max-width:none}.nav-tabs{border-bottom:1px solid #ddd}.nav-tabs>li{float:left;margin-bottom:-1px}.nav-tabs>li>a{margin-right:2px;line-height:1.42857143;border:1px solid transparent;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover{border-color:#eee #eee #ddd}.nav-tabs>li.active>a,.nav-tabs>li.active>a:focus,.nav-tabs>li.active>a:hover{color:#555;cursor:default;background-color:#fff;border:1px solid #ddd;border-bottom-color:transparent}.nav-tabs.nav-justified{width:100%;border-bottom:0}.nav-tabs.nav-justified>li{float:none}.nav-tabs.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-tabs.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-tabs.nav-justified>li{display:table-cell;width:1%}.nav-tabs.nav-justified>li>a{margin-bottom:0}}.nav-tabs.nav-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs.nav-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border-bottom-color:#fff}}.nav-pills>li{float:left}.nav-pills>li>a{border-radius:4px}.nav-pills>li+li{margin-left:2px}.nav-pills>li.active>a,.nav-pills>li.active>a:focus,.nav-pills>li.active>a:hover{color:#fff;background-color:#337ab7}.nav-stacked>li{float:none}.nav-stacked>li+li{margin-top:2px;margin-left:0}.nav-justified{width:100%}.nav-justified>li{float:none}.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-justified>li{display:table-cell;width:1%}.nav-justified>li>a{margin-bottom:0}}.nav-tabs-justified{border-bottom:0}.nav-tabs-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border-bottom-color:#fff}}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.nav-tabs .dropdown-menu{margin-top:-1px;border-top-left-radius:0;border-top-right-radius:0}.navbar{position:relative;min-height:50px;margin-bottom:20px;border:1px solid transparent}@media (min-width:768px){.navbar{border-radius:4px}}@media (min-width:768px){.navbar-header{float:left}}.navbar-collapse{padding-right:15px;padding-left:15px;overflow-x:visible;-webkit-overflow-scrolling:touch;border-top:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1)}.navbar-collapse.in{overflow-y:auto}@media (min-width:768px){.navbar-collapse{width:auto;border-top:0;-webkit-box-shadow:none;box-shadow:none}.navbar-collapse.collapse{display:block!important;height:auto!important;padding-bottom:0;overflow:visible!important}.navbar-collapse.in{overflow-y:visible}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse,.navbar-static-top .navbar-collapse{padding-right:0;padding-left:0}}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:340px}@media (max-device-width:480px) and (orientation:landscape){.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:200px}}.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:0;margin-left:0}}.navbar-static-top{z-index:1000;border-width:0 0 1px}@media (min-width:768px){.navbar-static-top{border-radius:0}}.navbar-fixed-bottom,.navbar-fixed-top{position:fixed;right:0;left:0;z-index:1030}@media (min-width:768px){.navbar-fixed-bottom,.navbar-fixed-top{border-radius:0}}.navbar-fixed-top{top:0;border-width:0 0 1px}.navbar-fixed-bottom{bottom:0;margin-bottom:0;border-width:1px 0 0}.navbar-brand{float:left;height:50px;padding:15px 15px;font-size:18px;line-height:20px}.navbar-brand:focus,.navbar-brand:hover{text-decoration:none}.navbar-brand>img{display:block}@media (min-width:768px){.navbar>.container .navbar-brand,.navbar>.container-fluid .navbar-brand{margin-left:-15px}}.navbar-toggle{position:relative;float:right;padding:9px 10px;margin-top:8px;margin-right:15px;margin-bottom:8px;background-color:transparent;background-image:none;border:1px solid transparent;border-radius:4px}.navbar-toggle:focus{outline:0}.navbar-toggle .icon-bar{display:block;width:22px;height:2px;border-radius:1px}.navbar-toggle .icon-bar+.icon-bar{margin-top:4px}@media (min-width:768px){.navbar-toggle{display:none}}.navbar-nav{margin:7.5px -15px}.navbar-nav>li>a{padding-top:10px;padding-bottom:10px;line-height:20px}@media (max-width:767px){.navbar-nav .open .dropdown-menu{position:static;float:none;width:auto;margin-top:0;background-color:transparent;border:0;-webkit-box-shadow:none;box-shadow:none}.navbar-nav .open .dropdown-menu .dropdown-header,.navbar-nav .open .dropdown-menu>li>a{padding:5px 15px 5px 25px}.navbar-nav .open .dropdown-menu>li>a{line-height:20px}.navbar-nav .open .dropdown-menu>li>a:focus,.navbar-nav .open .dropdown-menu>li>a:hover{background-image:none}}@media (min-width:768px){.navbar-nav{float:left;margin:0}.navbar-nav>li{float:left}.navbar-nav>li>a{padding-top:15px;padding-bottom:15px}}.navbar-form{padding:10px 15px;margin-top:8px;margin-right:-15px;margin-bottom:8px;margin-left:-15px;border-top:1px solid transparent;border-bottom:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1)}@media (min-width:768px){.navbar-form .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.navbar-form .form-control{display:inline-block;width:auto;vertical-align:middle}.navbar-form .form-control-static{display:inline-block}.navbar-form .input-group{display:inline-table;vertical-align:middle}.navbar-form .input-group .form-control,.navbar-form .input-group .input-group-addon,.navbar-form .input-group .input-group-btn{width:auto}.navbar-form .input-group>.form-control{width:100%}.navbar-form .control-label{margin-bottom:0;vertical-align:middle}.navbar-form .checkbox,.navbar-form .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.navbar-form .checkbox label,.navbar-form .radio label{padding-left:0}.navbar-form .checkbox input[type=checkbox],.navbar-form .radio input[type=radio]{position:relative;margin-left:0}.navbar-form .has-feedback .form-control-feedback{top:0}}@media (max-width:767px){.navbar-form .form-group{margin-bottom:5px}.navbar-form .form-group:last-child{margin-bottom:0}}@media (min-width:768px){.navbar-form{width:auto;padding-top:0;padding-bottom:0;margin-right:0;margin-left:0;border:0;-webkit-box-shadow:none;box-shadow:none}}.navbar-nav>li>.dropdown-menu{margin-top:0;border-top-left-radius:0;border-top-right-radius:0}.navbar-fixed-bottom .navbar-nav>li>.dropdown-menu{margin-bottom:0;border-top-left-radius:4px;border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.navbar-btn{margin-top:8px;margin-bottom:8px}.navbar-btn.btn-sm{margin-top:10px;margin-bottom:10px}.navbar-btn.btn-xs{margin-top:14px;margin-bottom:14px}.navbar-text{margin-top:15px;margin-bottom:15px}@media (min-width:768px){.navbar-text{float:left;margin-right:15px;margin-left:15px}}@media (min-width:768px){.navbar-left{float:left!important}.navbar-right{float:right!important;margin-right:-15px}.navbar-right~.navbar-right{margin-right:0}}.navbar-default{background-color:#f8f8f8;border-color:#e7e7e7}.navbar-default .navbar-brand{color:#777}.navbar-default .navbar-brand:focus,.navbar-default .navbar-brand:hover{color:#5e5e5e;background-color:transparent}.navbar-default .navbar-text{color:#777}.navbar-default .navbar-nav>li>a{color:#777}.navbar-default .navbar-nav>li>a:focus,.navbar-default .navbar-nav>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav>.active>a,.navbar-default .navbar-nav>.active>a:focus,.navbar-default .navbar-nav>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav>.disabled>a,.navbar-default .navbar-nav>.disabled>a:focus,.navbar-default .navbar-nav>.disabled>a:hover{color:#ccc;background-color:transparent}.navbar-default .navbar-toggle{border-color:#ddd}.navbar-default .navbar-toggle:focus,.navbar-default .navbar-toggle:hover{background-color:#ddd}.navbar-default .navbar-toggle .icon-bar{background-color:#888}.navbar-default .navbar-collapse,.navbar-default .navbar-form{border-color:#e7e7e7}.navbar-default .navbar-nav>.open>a,.navbar-default .navbar-nav>.open>a:focus,.navbar-default .navbar-nav>.open>a:hover{color:#555;background-color:#e7e7e7}@media (max-width:767px){.navbar-default .navbar-nav .open .dropdown-menu>li>a{color:#777}.navbar-default .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav .open .dropdown-menu>.active>a,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#ccc;background-color:transparent}}.navbar-default .navbar-link{color:#777}.navbar-default .navbar-link:hover{color:#333}.navbar-default .btn-link{color:#777}.navbar-default .btn-link:focus,.navbar-default .btn-link:hover{color:#333}.navbar-default .btn-link[disabled]:focus,.navbar-default .btn-link[disabled]:hover,fieldset[disabled] .navbar-default .btn-link:focus,fieldset[disabled] .navbar-default .btn-link:hover{color:#ccc}.navbar-inverse{background-color:#222;border-color:#080808}.navbar-inverse .navbar-brand{color:#9d9d9d}.navbar-inverse .navbar-brand:focus,.navbar-inverse .navbar-brand:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-text{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a:focus,.navbar-inverse .navbar-nav>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav>.active>a,.navbar-inverse .navbar-nav>.active>a:focus,.navbar-inverse .navbar-nav>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav>.disabled>a,.navbar-inverse .navbar-nav>.disabled>a:focus,.navbar-inverse .navbar-nav>.disabled>a:hover{color:#444;background-color:transparent}.navbar-inverse .navbar-toggle{border-color:#333}.navbar-inverse .navbar-toggle:focus,.navbar-inverse .navbar-toggle:hover{background-color:#333}.navbar-inverse .navbar-toggle .icon-bar{background-color:#fff}.navbar-inverse .navbar-collapse,.navbar-inverse .navbar-form{border-color:#101010}.navbar-inverse .navbar-nav>.open>a,.navbar-inverse .navbar-nav>.open>a:focus,.navbar-inverse .navbar-nav>.open>a:hover{color:#fff;background-color:#080808}@media (max-width:767px){.navbar-inverse .navbar-nav .open .dropdown-menu>.dropdown-header{border-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu .divider{background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#444;background-color:transparent}}.navbar-inverse .navbar-link{color:#9d9d9d}.navbar-inverse .navbar-link:hover{color:#fff}.navbar-inverse .btn-link{color:#9d9d9d}.navbar-inverse .btn-link:focus,.navbar-inverse .btn-link:hover{color:#fff}.navbar-inverse .btn-link[disabled]:focus,.navbar-inverse .btn-link[disabled]:hover,fieldset[disabled] .navbar-inverse .btn-link:focus,fieldset[disabled] .navbar-inverse .btn-link:hover{color:#444}.breadcrumb{padding:8px 15px;margin-bottom:20px;list-style:none;background-color:#f5f5f5;border-radius:4px}.breadcrumb>li{display:inline-block}.breadcrumb>li+li:before{padding:0 5px;color:#ccc;content:"/\00a0"}.breadcrumb>.active{color:#777}.pagination{display:inline-block;padding-left:0;margin:20px 0;border-radius:4px}.pagination>li{display:inline}.pagination>li>a,.pagination>li>span{position:relative;float:left;padding:6px 12px;margin-left:-1px;line-height:1.42857143;color:#337ab7;text-decoration:none;background-color:#fff;border:1px solid #ddd}.pagination>li:first-child>a,.pagination>li:first-child>span{margin-left:0;border-top-left-radius:4px;border-bottom-left-radius:4px}.pagination>li:last-child>a,.pagination>li:last-child>span{border-top-right-radius:4px;border-bottom-right-radius:4px}.pagination>li>a:focus,.pagination>li>a:hover,.pagination>li>span:focus,.pagination>li>span:hover{z-index:3;color:#23527c;background-color:#eee;border-color:#ddd}.pagination>.active>a,.pagination>.active>a:focus,.pagination>.active>a:hover,.pagination>.active>span,.pagination>.active>span:focus,.pagination>.active>span:hover{z-index:2;color:#fff;cursor:default;background-color:#337ab7;border-color:#337ab7}.pagination>.disabled>a,.pagination>.disabled>a:focus,.pagination>.disabled>a:hover,.pagination>.disabled>span,.pagination>.disabled>span:focus,.pagination>.disabled>span:hover{color:#777;cursor:not-allowed;background-color:#fff;border-color:#ddd}.pagination-lg>li>a,.pagination-lg>li>span{padding:10px 16px;font-size:18px;line-height:1.3333333}.pagination-lg>li:first-child>a,.pagination-lg>li:first-child>span{border-top-left-radius:6px;border-bottom-left-radius:6px}.pagination-lg>li:last-child>a,.pagination-lg>li:last-child>span{border-top-right-radius:6px;border-bottom-right-radius:6px}.pagination-sm>li>a,.pagination-sm>li>span{padding:5px 10px;font-size:12px;line-height:1.5}.pagination-sm>li:first-child>a,.pagination-sm>li:first-child>span{border-top-left-radius:3px;border-bottom-left-radius:3px}.pagination-sm>li:last-child>a,.pagination-sm>li:last-child>span{border-top-right-radius:3px;border-bottom-right-radius:3px}.pager{padding-left:0;margin:20px 0;text-align:center;list-style:none}.pager li{display:inline}.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px}.pager li>a:focus,.pager li>a:hover{text-decoration:none;background-color:#eee}.pager .next>a,.pager .next>span{float:right}.pager .previous>a,.pager .previous>span{float:left}.pager .disabled>a,.pager .disabled>a:focus,.pager .disabled>a:hover,.pager .disabled>span{color:#777;cursor:not-allowed;background-color:#fff}.label{display:inline;padding:.2em .6em .3em;font-size:75%;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:.25em}a.label:focus,a.label:hover{color:#fff;text-decoration:none;cursor:pointer}.label:empty{display:none}.btn .label{position:relative;top:-1px}.label-default{background-color:#777}.label-default[href]:focus,.label-default[href]:hover{background-color:#5e5e5e}.label-primary{background-color:#337ab7}.label-primary[href]:focus,.label-primary[href]:hover{background-color:#286090}.label-success{background-color:#5cb85c}.label-success[href]:focus,.label-success[href]:hover{background-color:#449d44}.label-info{background-color:#5bc0de}.label-info[href]:focus,.label-info[href]:hover{background-color:#31b0d5}.label-warning{background-color:#f0ad4e}.label-warning[href]:focus,.label-warning[href]:hover{background-color:#ec971f}.label-danger{background-color:#d9534f}.label-danger[href]:focus,.label-danger[href]:hover{background-color:#c9302c}.badge{display:inline-block;min-width:10px;padding:3px 7px;font-size:12px;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:middle;background-color:#777;border-radius:10px}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.btn-group-xs>.btn .badge,.btn-xs .badge{top:0;padding:1px 5px}a.badge:focus,a.badge:hover{color:#fff;text-decoration:none;cursor:pointer}.list-group-item.active>.badge,.nav-pills>.active>a>.badge{color:#337ab7;background-color:#fff}.list-group-item>.badge{float:right}.list-group-item>.badge+.badge{margin-right:5px}.nav-pills>li>a>.badge{margin-left:3px}.jumbotron{padding-top:30px;padding-bottom:30px;margin-bottom:30px;color:inherit;background-color:#eee}.jumbotron .h1,.jumbotron h1{color:inherit}.jumbotron p{margin-bottom:15px;font-size:21px;font-weight:200}.jumbotron>hr{border-top-color:#d5d5d5}.container .jumbotron,.container-fluid .jumbotron{border-radius:6px}.jumbotron .container{max-width:100%}@media screen and (min-width:768px){.jumbotron{padding-top:48px;padding-bottom:48px}.container .jumbotron,.container-fluid .jumbotron{padding-right:60px;padding-left:60px}.jumbotron .h1,.jumbotron h1{font-size:63px}}.thumbnail{display:block;padding:4px;margin-bottom:20px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:border .2s ease-in-out;-o-transition:border .2s ease-in-out;transition:border .2s ease-in-out}.thumbnail a>img,.thumbnail>img{margin-right:auto;margin-left:auto}a.thumbnail.active,a.thumbnail:focus,a.thumbnail:hover{border-color:#337ab7}.thumbnail .caption{padding:9px;color:#333}.alert{padding:15px;margin-bottom:20px;border:1px solid transparent;border-radius:4px}.alert h4{margin-top:0;color:inherit}.alert .alert-link{font-weight:700}.alert>p,.alert>ul{margin-bottom:0}.alert>p+p{margin-top:5px}.alert-dismissable,.alert-dismissible{padding-right:35px}.alert-dismissable .close,.alert-dismissible .close{position:relative;top:-2px;right:-21px;color:inherit}.alert-success{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.alert-success hr{border-top-color:#c9e2b3}.alert-success .alert-link{color:#2b542c}.alert-info{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.alert-info hr{border-top-color:#a6e1ec}.alert-info .alert-link{color:#245269}.alert-warning{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.alert-warning hr{border-top-color:#f7e1b5}.alert-warning .alert-link{color:#66512c}.alert-danger{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.alert-danger hr{border-top-color:#e4b9c0}.alert-danger .alert-link{color:#843534}@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}.progress{height:20px;margin-bottom:20px;overflow:hidden;background-color:#f5f5f5;border-radius:4px;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,.1);box-shadow:inset 0 1px 2px rgba(0,0,0,.1)}.progress-bar{float:left;width:0;height:100%;font-size:12px;line-height:20px;color:#fff;text-align:center;background-color:#337ab7;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);-webkit-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}.progress-bar-striped,.progress-striped .progress-bar{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);-webkit-background-size:40px 40px;background-size:40px 40px}.progress-bar.active,.progress.active .progress-bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}.progress-bar-success{background-color:#5cb85c}.progress-striped .progress-bar-success{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-info{background-color:#5bc0de}.progress-striped .progress-bar-info{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-warning{background-color:#f0ad4e}.progress-striped .progress-bar-warning{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-danger{background-color:#d9534f}.progress-striped .progress-bar-danger{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.media{margin-top:15px}.media:first-child{margin-top:0}.media,.media-body{overflow:hidden;zoom:1}.media-body{width:10000px}.media-object{display:block}.media-object.img-thumbnail{max-width:none}.media-right,.media>.pull-right{padding-left:10px}.media-left,.media>.pull-left{padding-right:10px}.media-body,.media-left,.media-right{display:table-cell;vertical-align:top}.media-middle{vertical-align:middle}.media-bottom{vertical-align:bottom}.media-heading{margin-top:0;margin-bottom:5px}.media-list{padding-left:0;list-style:none}.list-group{padding-left:0;margin-bottom:20px}.list-group-item{position:relative;display:block;padding:10px 15px;margin-bottom:-1px;background-color:#fff;border:1px solid #ddd}.list-group-item:first-child{border-top-left-radius:4px;border-top-right-radius:4px}.list-group-item:last-child{margin-bottom:0;border-bottom-right-radius:4px;border-bottom-left-radius:4px}a.list-group-item,button.list-group-item{color:#555}a.list-group-item .list-group-item-heading,button.list-group-item .list-group-item-heading{color:#333}a.list-group-item:focus,a.list-group-item:hover,button.list-group-item:focus,button.list-group-item:hover{color:#555;text-decoration:none;background-color:#f5f5f5}button.list-group-item{width:100%;text-align:left}.list-group-item.disabled,.list-group-item.disabled:focus,.list-group-item.disabled:hover{color:#777;cursor:not-allowed;background-color:#eee}.list-group-item.disabled .list-group-item-heading,.list-group-item.disabled:focus .list-group-item-heading,.list-group-item.disabled:hover .list-group-item-heading{color:inherit}.list-group-item.disabled .list-group-item-text,.list-group-item.disabled:focus .list-group-item-text,.list-group-item.disabled:hover .list-group-item-text{color:#777}.list-group-item.active,.list-group-item.active:focus,.list-group-item.active:hover{z-index:2;color:#fff;background-color:#337ab7;border-color:#337ab7}.list-group-item.active .list-group-item-heading,.list-group-item.active .list-group-item-heading>.small,.list-group-item.active .list-group-item-heading>small,.list-group-item.active:focus .list-group-item-heading,.list-group-item.active:focus .list-group-item-heading>.small,.list-group-item.active:focus .list-group-item-heading>small,.list-group-item.active:hover .list-group-item-heading,.list-group-item.active:hover .list-group-item-heading>.small,.list-group-item.active:hover .list-group-item-heading>small{color:inherit}.list-group-item.active .list-group-item-text,.list-group-item.active:focus .list-group-item-text,.list-group-item.active:hover .list-group-item-text{color:#c7ddef}.list-group-item-success{color:#3c763d;background-color:#dff0d8}a.list-group-item-success,button.list-group-item-success{color:#3c763d}a.list-group-item-success .list-group-item-heading,button.list-group-item-success .list-group-item-heading{color:inherit}a.list-group-item-success:focus,a.list-group-item-success:hover,button.list-group-item-success:focus,button.list-group-item-success:hover{color:#3c763d;background-color:#d0e9c6}a.list-group-item-success.active,a.list-group-item-success.active:focus,a.list-group-item-success.active:hover,button.list-group-item-success.active,button.list-group-item-success.active:focus,button.list-group-item-success.active:hover{color:#fff;background-color:#3c763d;border-color:#3c763d}.list-group-item-info{color:#31708f;background-color:#d9edf7}a.list-group-item-info,button.list-group-item-info{color:#31708f}a.list-group-item-info .list-group-item-heading,button.list-group-item-info .list-group-item-heading{color:inherit}a.list-group-item-info:focus,a.list-group-item-info:hover,button.list-group-item-info:focus,button.list-group-item-info:hover{color:#31708f;background-color:#c4e3f3}a.list-group-item-info.active,a.list-group-item-info.active:focus,a.list-group-item-info.active:hover,button.list-group-item-info.active,button.list-group-item-info.active:focus,button.list-group-item-info.active:hover{color:#fff;background-color:#31708f;border-color:#31708f}.list-group-item-warning{color:#8a6d3b;background-color:#fcf8e3}a.list-group-item-warning,button.list-group-item-warning{color:#8a6d3b}a.list-group-item-warning .list-group-item-heading,button.list-group-item-warning .list-group-item-heading{color:inherit}a.list-group-item-warning:focus,a.list-group-item-warning:hover,button.list-group-item-warning:focus,button.list-group-item-warning:hover{color:#8a6d3b;background-color:#faf2cc}a.list-group-item-warning.active,a.list-group-item-warning.active:focus,a.list-group-item-warning.active:hover,button.list-group-item-warning.active,button.list-group-item-warning.active:focus,button.list-group-item-warning.active:hover{color:#fff;background-color:#8a6d3b;border-color:#8a6d3b}.list-group-item-danger{color:#a94442;background-color:#f2dede}a.list-group-item-danger,button.list-group-item-danger{color:#a94442}a.list-group-item-danger .list-group-item-heading,button.list-group-item-danger .list-group-item-heading{color:inherit}a.list-group-item-danger:focus,a.list-group-item-danger:hover,button.list-group-item-danger:focus,button.list-group-item-danger:hover{color:#a94442;background-color:#ebcccc}a.list-group-item-danger.active,a.list-group-item-danger.active:focus,a.list-group-item-danger.active:hover,button.list-group-item-danger.active,button.list-group-item-danger.active:focus,button.list-group-item-danger.active:hover{color:#fff;background-color:#a94442;border-color:#a94442}.list-group-item-heading{margin-top:0;margin-bottom:5px}.list-group-item-text{margin-bottom:0;line-height:1.3}.panel{margin-bottom:20px;background-color:#fff;border:1px solid transparent;border-radius:4px;-webkit-box-shadow:0 1px 1px rgba(0,0,0,.05);box-shadow:0 1px 1px rgba(0,0,0,.05)}.panel-body{padding:15px}.panel-heading{padding:10px 15px;border-bottom:1px solid transparent;border-top-left-radius:3px;border-top-right-radius:3px}.panel-heading>.dropdown .dropdown-toggle{color:inherit}.panel-title{margin-top:0;margin-bottom:0;font-size:16px;color:inherit}.panel-title>.small,.panel-title>.small>a,.panel-title>a,.panel-title>small,.panel-title>small>a{color:inherit}.panel-footer{padding:10px 15px;background-color:#f5f5f5;border-top:1px solid #ddd;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.list-group,.panel>.panel-collapse>.list-group{margin-bottom:0}.panel>.list-group .list-group-item,.panel>.panel-collapse>.list-group .list-group-item{border-width:1px 0;border-radius:0}.panel>.list-group:first-child .list-group-item:first-child,.panel>.panel-collapse>.list-group:first-child .list-group-item:first-child{border-top:0;border-top-left-radius:3px;border-top-right-radius:3px}.panel>.list-group:last-child .list-group-item:last-child,.panel>.panel-collapse>.list-group:last-child .list-group-item:last-child{border-bottom:0;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.panel-heading+.panel-collapse>.list-group .list-group-item:first-child{border-top-left-radius:0;border-top-right-radius:0}.panel-heading+.list-group .list-group-item:first-child{border-top-width:0}.list-group+.panel-footer{border-top-width:0}.panel>.panel-collapse>.table,.panel>.table,.panel>.table-responsive>.table{margin-bottom:0}.panel>.panel-collapse>.table caption,.panel>.table caption,.panel>.table-responsive>.table caption{padding-right:15px;padding-left:15px}.panel>.table-responsive:first-child>.table:first-child,.panel>.table:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child,.panel>.table:first-child>thead:first-child>tr:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table:first-child>thead:first-child>tr:first-child th:first-child{border-top-left-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table:first-child>thead:first-child>tr:first-child th:last-child{border-top-right-radius:3px}.panel>.table-responsive:last-child>.table:last-child,.panel>.table:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:first-child{border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:last-child{border-bottom-right-radius:3px}.panel>.panel-body+.table,.panel>.panel-body+.table-responsive,.panel>.table+.panel-body,.panel>.table-responsive+.panel-body{border-top:1px solid #ddd}.panel>.table>tbody:first-child>tr:first-child td,.panel>.table>tbody:first-child>tr:first-child th{border-top:0}.panel>.table-bordered,.panel>.table-responsive>.table-bordered{border:0}.panel>.table-bordered>tbody>tr>td:first-child,.panel>.table-bordered>tbody>tr>th:first-child,.panel>.table-bordered>tfoot>tr>td:first-child,.panel>.table-bordered>tfoot>tr>th:first-child,.panel>.table-bordered>thead>tr>td:first-child,.panel>.table-bordered>thead>tr>th:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:first-child,.panel>.table-responsive>.table-bordered>thead>tr>td:first-child,.panel>.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.panel>.table-bordered>tbody>tr>td:last-child,.panel>.table-bordered>tbody>tr>th:last-child,.panel>.table-bordered>tfoot>tr>td:last-child,.panel>.table-bordered>tfoot>tr>th:last-child,.panel>.table-bordered>thead>tr>td:last-child,.panel>.table-bordered>thead>tr>th:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:last-child,.panel>.table-responsive>.table-bordered>thead>tr>td:last-child,.panel>.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.panel>.table-bordered>tbody>tr:first-child>td,.panel>.table-bordered>tbody>tr:first-child>th,.panel>.table-bordered>thead>tr:first-child>td,.panel>.table-bordered>thead>tr:first-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>th,.panel>.table-responsive>.table-bordered>thead>tr:first-child>td,.panel>.table-responsive>.table-bordered>thead>tr:first-child>th{border-bottom:0}.panel>.table-bordered>tbody>tr:last-child>td,.panel>.table-bordered>tbody>tr:last-child>th,.panel>.table-bordered>tfoot>tr:last-child>td,.panel>.table-bordered>tfoot>tr:last-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>th,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>td,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}.panel>.table-responsive{margin-bottom:0;border:0}.panel-group{margin-bottom:20px}.panel-group .panel{margin-bottom:0;border-radius:4px}.panel-group .panel+.panel{margin-top:5px}.panel-group .panel-heading{border-bottom:0}.panel-group .panel-heading+.panel-collapse>.list-group,.panel-group .panel-heading+.panel-collapse>.panel-body{border-top:1px solid #ddd}.panel-group .panel-footer{border-top:0}.panel-group .panel-footer+.panel-collapse .panel-body{border-bottom:1px solid #ddd}.panel-default{border-color:#ddd}.panel-default>.panel-heading{color:#333;background-color:#f5f5f5;border-color:#ddd}.panel-default>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ddd}.panel-default>.panel-heading .badge{color:#f5f5f5;background-color:#333}.panel-default>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ddd}.panel-primary{border-color:#337ab7}.panel-primary>.panel-heading{color:#fff;background-color:#337ab7;border-color:#337ab7}.panel-primary>.panel-heading+.panel-collapse>.panel-body{border-top-color:#337ab7}.panel-primary>.panel-heading .badge{color:#337ab7;background-color:#fff}.panel-primary>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#337ab7}.panel-success{border-color:#d6e9c6}.panel-success>.panel-heading{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.panel-success>.panel-heading+.panel-collapse>.panel-body{border-top-color:#d6e9c6}.panel-success>.panel-heading .badge{color:#dff0d8;background-color:#3c763d}.panel-success>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#d6e9c6}.panel-info{border-color:#bce8f1}.panel-info>.panel-heading{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.panel-info>.panel-heading+.panel-collapse>.panel-body{border-top-color:#bce8f1}.panel-info>.panel-heading .badge{color:#d9edf7;background-color:#31708f}.panel-info>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#bce8f1}.panel-warning{border-color:#faebcc}.panel-warning>.panel-heading{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.panel-warning>.panel-heading+.panel-collapse>.panel-body{border-top-color:#faebcc}.panel-warning>.panel-heading .badge{color:#fcf8e3;background-color:#8a6d3b}.panel-warning>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#faebcc}.panel-danger{border-color:#ebccd1}.panel-danger>.panel-heading{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.panel-danger>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ebccd1}.panel-danger>.panel-heading .badge{color:#f2dede;background-color:#a94442}.panel-danger>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ebccd1}.embed-responsive{position:relative;display:block;height:0;padding:0;overflow:hidden}.embed-responsive .embed-responsive-item,.embed-responsive embed,.embed-responsive iframe,.embed-responsive object,.embed-responsive video{position:absolute;top:0;bottom:0;left:0;width:100%;height:100%;border:0}.embed-responsive-16by9{padding-bottom:56.25%}.embed-responsive-4by3{padding-bottom:75%}.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.05);box-shadow:inset 0 1px 1px rgba(0,0,0,.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,.15)}.well-lg{padding:24px;border-radius:6px}.well-sm{padding:9px;border-radius:3px}.close{float:right;font-size:21px;font-weight:700;line-height:1;color:#000;text-shadow:0 1px 0 #fff;filter:alpha(opacity=20);opacity:.2}.close:focus,.close:hover{color:#000;text-decoration:none;cursor:pointer;filter:alpha(opacity=50);opacity:.5}button.close{-webkit-appearance:none;padding:0;cursor:pointer;background:0 0;border:0}.modal-open{overflow:hidden}.modal{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1050;display:none;overflow:hidden;-webkit-overflow-scrolling:touch;outline:0}.modal.fade .modal-dialog{-webkit-transition:-webkit-transform .3s ease-out;-o-transition:-o-transform .3s ease-out;transition:transform .3s ease-out;-webkit-transform:translate(0,-25%);-ms-transform:translate(0,-25%);-o-transform:translate(0,-25%);transform:translate(0,-25%)}.modal.in .modal-dialog{-webkit-transform:translate(0,0);-ms-transform:translate(0,0);-o-transform:translate(0,0);transform:translate(0,0)}.modal-open .modal{overflow-x:hidden;overflow-y:auto}.modal-dialog{position:relative;width:auto;margin:10px}.modal-content{position:relative;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #999;border:1px solid rgba(0,0,0,.2);border-radius:6px;outline:0;-webkit-box-shadow:0 3px 9px rgba(0,0,0,.5);box-shadow:0 3px 9px rgba(0,0,0,.5)}.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{filter:alpha(opacity=0);opacity:0}.modal-backdrop.in{filter:alpha(opacity=50);opacity:.5}.modal-header{min-height:16.43px;padding:15px;border-bottom:1px solid #e5e5e5}.modal-header .close{margin-top:-2px}.modal-title{margin:0;line-height:1.42857143}.modal-body{position:relative;padding:15px}.modal-footer{padding:15px;text-align:right;border-top:1px solid #e5e5e5}.modal-footer .btn+.btn{margin-bottom:0;margin-left:5px}.modal-footer .btn-group .btn+.btn{margin-left:-1px}.modal-footer .btn-block+.btn-block{margin-left:0}.modal-scrollbar-measure{position:absolute;top:-9999px;width:50px;height:50px;overflow:scroll}@media (min-width:768px){.modal-dialog{width:600px;margin:30px auto}.modal-content{-webkit-box-shadow:0 5px 15px rgba(0,0,0,.5);box-shadow:0 5px 15px rgba(0,0,0,.5)}.modal-sm{width:300px}}@media (min-width:992px){.modal-lg{width:900px}}.tooltip{position:absolute;z-index:1070;display:block;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:12px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;filter:alpha(opacity=0);opacity:0;line-break:auto}.tooltip.in{filter:alpha(opacity=90);opacity:.9}.tooltip.top{padding:5px 0;margin-top:-3px}.tooltip.right{padding:0 5px;margin-left:3px}.tooltip.bottom{padding:5px 0;margin-top:3px}.tooltip.left{padding:0 5px;margin-left:-3px}.tooltip-inner{max-width:200px;padding:3px 8px;color:#fff;text-align:center;background-color:#000;border-radius:4px}.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-left .tooltip-arrow{right:5px;bottom:0;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-right .tooltip-arrow{bottom:0;left:5px;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000}.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000}.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-left .tooltip-arrow{top:0;right:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-right .tooltip-arrow{top:0;left:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.popover{position:absolute;top:0;left:0;z-index:1060;display:none;max-width:276px;padding:1px;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.2);border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,.2);box-shadow:0 5px 10px rgba(0,0,0,.2);line-break:auto}.popover.top{margin-top:-10px}.popover.right{margin-left:10px}.popover.bottom{margin-top:10px}.popover.left{margin-left:-10px}.popover-title{padding:8px 14px;margin:0;font-size:14px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;border-radius:5px 5px 0 0}.popover-content{padding:9px 14px}.popover>.arrow,.popover>.arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}.popover>.arrow{border-width:11px}.popover>.arrow:after{content:"";border-width:10px}.popover.top>.arrow{bottom:-11px;left:50%;margin-left:-11px;border-top-color:#999;border-top-color:rgba(0,0,0,.25);border-bottom-width:0}.popover.top>.arrow:after{bottom:1px;margin-left:-10px;content:" ";border-top-color:#fff;border-bottom-width:0}.popover.right>.arrow{top:50%;left:-11px;margin-top:-11px;border-right-color:#999;border-right-color:rgba(0,0,0,.25);border-left-width:0}.popover.right>.arrow:after{bottom:-10px;left:1px;content:" ";border-right-color:#fff;border-left-width:0}.popover.bottom>.arrow{top:-11px;left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,.25)}.popover.bottom>.arrow:after{top:1px;margin-left:-10px;content:" ";border-top-width:0;border-bottom-color:#fff}.popover.left>.arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0,0,0,.25)}.popover.left>.arrow:after{right:1px;bottom:-10px;content:" ";border-right-width:0;border-left-color:#fff}.carousel{position:relative}.carousel-inner{position:relative;width:100%;overflow:hidden}.carousel-inner>.item{position:relative;display:none;-webkit-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>a>img,.carousel-inner>.item>img{line-height:1}@media all and (transform-3d),(-webkit-transform-3d){.carousel-inner>.item{-webkit-transition:-webkit-transform .6s ease-in-out;-o-transition:-o-transform .6s ease-in-out;transition:transform .6s ease-in-out;-webkit-backface-visibility:hidden;backface-visibility:hidden;-webkit-perspective:1000px;perspective:1000px}.carousel-inner>.item.active.right,.carousel-inner>.item.next{left:0;-webkit-transform:translate3d(100%,0,0);transform:translate3d(100%,0,0)}.carousel-inner>.item.active.left,.carousel-inner>.item.prev{left:0;-webkit-transform:translate3d(-100%,0,0);transform:translate3d(-100%,0,0)}.carousel-inner>.item.active,.carousel-inner>.item.next.left,.carousel-inner>.item.prev.right{left:0;-webkit-transform:translate3d(0,0,0);transform:translate3d(0,0,0)}}.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}.carousel-inner>.active{left:0}.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%}.carousel-inner>.next{left:100%}.carousel-inner>.prev{left:-100%}.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0}.carousel-inner>.active.left{left:-100%}.carousel-inner>.active.right{left:100%}.carousel-control{position:absolute;top:0;bottom:0;left:0;width:15%;font-size:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6);filter:alpha(opacity=50);opacity:.5}.carousel-control.left{background-image:-webkit-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.5)),to(rgba(0,0,0,.0001)));background-image:linear-gradient(to right,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);background-repeat:repeat-x}.carousel-control.right{right:0;left:auto;background-image:-webkit-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.0001)),to(rgba(0,0,0,.5)));background-image:linear-gradient(to right,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);background-repeat:repeat-x}.carousel-control:focus,.carousel-control:hover{color:#fff;text-decoration:none;filter:alpha(opacity=90);outline:0;opacity:.9}.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{position:absolute;top:50%;z-index:5;display:inline-block;margin-top:-10px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{left:50%;margin-left:-10px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{right:50%;margin-right:-10px}.carousel-control .icon-next,.carousel-control .icon-prev{width:20px;height:20px;font-family:serif;line-height:1}.carousel-control .icon-prev:before{content:'\2039'}.carousel-control .icon-next:before{content:'\203a'}.carousel-indicators{position:absolute;bottom:10px;left:50%;z-index:15;width:60%;padding-left:0;margin-left:-30%;text-align:center;list-style:none}.carousel-indicators li{display:inline-block;width:10px;height:10px;margin:1px;text-indent:-999px;cursor:pointer;background-color:#000\9;background-color:rgba(0,0,0,0);border:1px solid #fff;border-radius:10px}.carousel-indicators .active{width:12px;height:12px;margin:0;background-color:#fff}.carousel-caption{position:absolute;right:15%;bottom:20px;left:15%;z-index:10;padding-top:20px;padding-bottom:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6)}.carousel-caption .btn{text-shadow:none}@media screen and (min-width:768px){.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{width:30px;height:30px;margin-top:-15px;font-size:30px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{margin-left:-15px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{margin-right:-15px}.carousel-caption{right:20%;left:20%;padding-bottom:30px}.carousel-indicators{bottom:20px}}.btn-group-vertical>.btn-group:after,.btn-group-vertical>.btn-group:before,.btn-toolbar:after,.btn-toolbar:before,.clearfix:after,.clearfix:before,.container-fluid:after,.container-fluid:before,.container:after,.container:before,.dl-horizontal dd:after,.dl-horizontal dd:before,.form-horizontal .form-group:after,.form-horizontal .form-group:before,.modal-footer:after,.modal-footer:before,.nav:after,.nav:before,.navbar-collapse:after,.navbar-collapse:before,.navbar-header:after,.navbar-header:before,.navbar:after,.navbar:before,.pager:after,.pager:before,.panel-body:after,.panel-body:before,.row:after,.row:before{display:table;content:" "}.btn-group-vertical>.btn-group:after,.btn-toolbar:after,.clearfix:after,.container-fluid:after,.container:after,.dl-horizontal dd:after,.form-horizontal .form-group:after,.modal-footer:after,.nav:after,.navbar-collapse:after,.navbar-header:after,.navbar:after,.pager:after,.panel-body:after,.row:after{clear:both}.center-block{display:block;margin-right:auto;margin-left:auto}.pull-right{float:right!important}.pull-left{float:left!important}.hide{display:none!important}.show{display:block!important}.invisible{visibility:hidden}.text-hide{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.hidden{display:none!important}.affix{position:fixed}@-ms-viewport{width:device-width}.visible-lg,.visible-md,.visible-sm,.visible-xs{display:none!important}.visible-lg-block,.visible-lg-inline,.visible-lg-inline-block,.visible-md-block,.visible-md-inline,.visible-md-inline-block,.visible-sm-block,.visible-sm-inline,.visible-sm-inline-block,.visible-xs-block,.visible-xs-inline,.visible-xs-inline-block{display:none!important}@media (max-width:767px){.visible-xs{display:block!important}table.visible-xs{display:table!important}tr.visible-xs{display:table-row!important}td.visible-xs,th.visible-xs{display:table-cell!important}}@media (max-width:767px){.visible-xs-block{display:block!important}}@media (max-width:767px){.visible-xs-inline{display:inline!important}}@media (max-width:767px){.visible-xs-inline-block{display:inline-block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm{display:block!important}table.visible-sm{display:table!important}tr.visible-sm{display:table-row!important}td.visible-sm,th.visible-sm{display:table-cell!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-block{display:block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline{display:inline!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline-block{display:inline-block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md{display:block!important}table.visible-md{display:table!important}tr.visible-md{display:table-row!important}td.visible-md,th.visible-md{display:table-cell!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-block{display:block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline{display:inline!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline-block{display:inline-block!important}}@media (min-width:1200px){.visible-lg{display:block!important}table.visible-lg{display:table!important}tr.visible-lg{display:table-row!important}td.visible-lg,th.visible-lg{display:table-cell!important}}@media (min-width:1200px){.visible-lg-block{display:block!important}}@media (min-width:1200px){.visible-lg-inline{display:inline!important}}@media (min-width:1200px){.visible-lg-inline-block{display:inline-block!important}}@media (max-width:767px){.hidden-xs{display:none!important}}@media (min-width:768px) and (max-width:991px){.hidden-sm{display:none!important}}@media (min-width:992px) and (max-width:1199px){.hidden-md{display:none!important}}@media (min-width:1200px){.hidden-lg{display:none!important}}.visible-print{display:none!important}@media print{.visible-print{display:block!important}table.visible-print{display:table!important}tr.visible-print{display:table-row!important}td.visible-print,th.visible-print{display:table-cell!important}}.visible-print-block{display:none!important}@media print{.visible-print-block{display:block!important}}.visible-print-inline{display:none!important}@media print{.visible-print-inline{display:inline!important}}.visible-print-inline-block{display:none!important}@media print{.visible-print-inline-block{display:inline-block!important}}@media print{.hidden-print{display:none!important}}
</style>
<script>/*!
 * Bootstrap v3.3.5 (http://getbootstrap.com)
 * Copyright 2011-2015 Twitter, Inc.
 * Licensed under the MIT license
 */
if("undefined"==typeof jQuery)throw new Error("Bootstrap's JavaScript requires jQuery");+function(a){"use strict";var b=a.fn.jquery.split(" ")[0].split(".");if(b[0]<2&&b[1]<9||1==b[0]&&9==b[1]&&b[2]<1)throw new Error("Bootstrap's JavaScript requires jQuery version 1.9.1 or higher")}(jQuery),+function(a){"use strict";function b(){var a=document.createElement("bootstrap"),b={WebkitTransition:"webkitTransitionEnd",MozTransition:"transitionend",OTransition:"oTransitionEnd otransitionend",transition:"transitionend"};for(var c in b)if(void 0!==a.style[c])return{end:b[c]};return!1}a.fn.emulateTransitionEnd=function(b){var c=!1,d=this;a(this).one("bsTransitionEnd",function(){c=!0});var e=function(){c||a(d).trigger(a.support.transition.end)};return setTimeout(e,b),this},a(function(){a.support.transition=b(),a.support.transition&&(a.event.special.bsTransitionEnd={bindType:a.support.transition.end,delegateType:a.support.transition.end,handle:function(b){return a(b.target).is(this)?b.handleObj.handler.apply(this,arguments):void 0}})})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var c=a(this),e=c.data("bs.alert");e||c.data("bs.alert",e=new d(this)),"string"==typeof b&&e[b].call(c)})}var c='[data-dismiss="alert"]',d=function(b){a(b).on("click",c,this.close)};d.VERSION="3.3.5",d.TRANSITION_DURATION=150,d.prototype.close=function(b){function c(){g.detach().trigger("closed.bs.alert").remove()}var e=a(this),f=e.attr("data-target");f||(f=e.attr("href"),f=f&&f.replace(/.*(?=#[^\s]*$)/,""));var g=a(f);b&&b.preventDefault(),g.length||(g=e.closest(".alert")),g.trigger(b=a.Event("close.bs.alert")),b.isDefaultPrevented()||(g.removeClass("in"),a.support.transition&&g.hasClass("fade")?g.one("bsTransitionEnd",c).emulateTransitionEnd(d.TRANSITION_DURATION):c())};var e=a.fn.alert;a.fn.alert=b,a.fn.alert.Constructor=d,a.fn.alert.noConflict=function(){return a.fn.alert=e,this},a(document).on("click.bs.alert.data-api",c,d.prototype.close)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.button"),f="object"==typeof b&&b;e||d.data("bs.button",e=new c(this,f)),"toggle"==b?e.toggle():b&&e.setState(b)})}var c=function(b,d){this.$element=a(b),this.options=a.extend({},c.DEFAULTS,d),this.isLoading=!1};c.VERSION="3.3.5",c.DEFAULTS={loadingText:"loading..."},c.prototype.setState=function(b){var c="disabled",d=this.$element,e=d.is("input")?"val":"html",f=d.data();b+="Text",null==f.resetText&&d.data("resetText",d[e]()),setTimeout(a.proxy(function(){d[e](null==f[b]?this.options[b]:f[b]),"loadingText"==b?(this.isLoading=!0,d.addClass(c).attr(c,c)):this.isLoading&&(this.isLoading=!1,d.removeClass(c).removeAttr(c))},this),0)},c.prototype.toggle=function(){var a=!0,b=this.$element.closest('[data-toggle="buttons"]');if(b.length){var c=this.$element.find("input");"radio"==c.prop("type")?(c.prop("checked")&&(a=!1),b.find(".active").removeClass("active"),this.$element.addClass("active")):"checkbox"==c.prop("type")&&(c.prop("checked")!==this.$element.hasClass("active")&&(a=!1),this.$element.toggleClass("active")),c.prop("checked",this.$element.hasClass("active")),a&&c.trigger("change")}else this.$element.attr("aria-pressed",!this.$element.hasClass("active")),this.$element.toggleClass("active")};var d=a.fn.button;a.fn.button=b,a.fn.button.Constructor=c,a.fn.button.noConflict=function(){return a.fn.button=d,this},a(document).on("click.bs.button.data-api",'[data-toggle^="button"]',function(c){var d=a(c.target);d.hasClass("btn")||(d=d.closest(".btn")),b.call(d,"toggle"),a(c.target).is('input[type="radio"]')||a(c.target).is('input[type="checkbox"]')||c.preventDefault()}).on("focus.bs.button.data-api blur.bs.button.data-api",'[data-toggle^="button"]',function(b){a(b.target).closest(".btn").toggleClass("focus",/^focus(in)?$/.test(b.type))})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.carousel"),f=a.extend({},c.DEFAULTS,d.data(),"object"==typeof b&&b),g="string"==typeof b?b:f.slide;e||d.data("bs.carousel",e=new c(this,f)),"number"==typeof b?e.to(b):g?e[g]():f.interval&&e.pause().cycle()})}var c=function(b,c){this.$element=a(b),this.$indicators=this.$element.find(".carousel-indicators"),this.options=c,this.paused=null,this.sliding=null,this.interval=null,this.$active=null,this.$items=null,this.options.keyboard&&this.$element.on("keydown.bs.carousel",a.proxy(this.keydown,this)),"hover"==this.options.pause&&!("ontouchstart"in document.documentElement)&&this.$element.on("mouseenter.bs.carousel",a.proxy(this.pause,this)).on("mouseleave.bs.carousel",a.proxy(this.cycle,this))};c.VERSION="3.3.5",c.TRANSITION_DURATION=600,c.DEFAULTS={interval:5e3,pause:"hover",wrap:!0,keyboard:!0},c.prototype.keydown=function(a){if(!/input|textarea/i.test(a.target.tagName)){switch(a.which){case 37:this.prev();break;case 39:this.next();break;default:return}a.preventDefault()}},c.prototype.cycle=function(b){return b||(this.paused=!1),this.interval&&clearInterval(this.interval),this.options.interval&&!this.paused&&(this.interval=setInterval(a.proxy(this.next,this),this.options.interval)),this},c.prototype.getItemIndex=function(a){return this.$items=a.parent().children(".item"),this.$items.index(a||this.$active)},c.prototype.getItemForDirection=function(a,b){var c=this.getItemIndex(b),d="prev"==a&&0===c||"next"==a&&c==this.$items.length-1;if(d&&!this.options.wrap)return b;var e="prev"==a?-1:1,f=(c+e)%this.$items.length;return this.$items.eq(f)},c.prototype.to=function(a){var b=this,c=this.getItemIndex(this.$active=this.$element.find(".item.active"));return a>this.$items.length-1||0>a?void 0:this.sliding?this.$element.one("slid.bs.carousel",function(){b.to(a)}):c==a?this.pause().cycle():this.slide(a>c?"next":"prev",this.$items.eq(a))},c.prototype.pause=function(b){return b||(this.paused=!0),this.$element.find(".next, .prev").length&&a.support.transition&&(this.$element.trigger(a.support.transition.end),this.cycle(!0)),this.interval=clearInterval(this.interval),this},c.prototype.next=function(){return this.sliding?void 0:this.slide("next")},c.prototype.prev=function(){return this.sliding?void 0:this.slide("prev")},c.prototype.slide=function(b,d){var e=this.$element.find(".item.active"),f=d||this.getItemForDirection(b,e),g=this.interval,h="next"==b?"left":"right",i=this;if(f.hasClass("active"))return this.sliding=!1;var j=f[0],k=a.Event("slide.bs.carousel",{relatedTarget:j,direction:h});if(this.$element.trigger(k),!k.isDefaultPrevented()){if(this.sliding=!0,g&&this.pause(),this.$indicators.length){this.$indicators.find(".active").removeClass("active");var l=a(this.$indicators.children()[this.getItemIndex(f)]);l&&l.addClass("active")}var m=a.Event("slid.bs.carousel",{relatedTarget:j,direction:h});return a.support.transition&&this.$element.hasClass("slide")?(f.addClass(b),f[0].offsetWidth,e.addClass(h),f.addClass(h),e.one("bsTransitionEnd",function(){f.removeClass([b,h].join(" ")).addClass("active"),e.removeClass(["active",h].join(" ")),i.sliding=!1,setTimeout(function(){i.$element.trigger(m)},0)}).emulateTransitionEnd(c.TRANSITION_DURATION)):(e.removeClass("active"),f.addClass("active"),this.sliding=!1,this.$element.trigger(m)),g&&this.cycle(),this}};var d=a.fn.carousel;a.fn.carousel=b,a.fn.carousel.Constructor=c,a.fn.carousel.noConflict=function(){return a.fn.carousel=d,this};var e=function(c){var d,e=a(this),f=a(e.attr("data-target")||(d=e.attr("href"))&&d.replace(/.*(?=#[^\s]+$)/,""));if(f.hasClass("carousel")){var g=a.extend({},f.data(),e.data()),h=e.attr("data-slide-to");h&&(g.interval=!1),b.call(f,g),h&&f.data("bs.carousel").to(h),c.preventDefault()}};a(document).on("click.bs.carousel.data-api","[data-slide]",e).on("click.bs.carousel.data-api","[data-slide-to]",e),a(window).on("load",function(){a('[data-ride="carousel"]').each(function(){var c=a(this);b.call(c,c.data())})})}(jQuery),+function(a){"use strict";function b(b){var c,d=b.attr("data-target")||(c=b.attr("href"))&&c.replace(/.*(?=#[^\s]+$)/,"");return a(d)}function c(b){return this.each(function(){var c=a(this),e=c.data("bs.collapse"),f=a.extend({},d.DEFAULTS,c.data(),"object"==typeof b&&b);!e&&f.toggle&&/show|hide/.test(b)&&(f.toggle=!1),e||c.data("bs.collapse",e=new d(this,f)),"string"==typeof b&&e[b]()})}var d=function(b,c){this.$element=a(b),this.options=a.extend({},d.DEFAULTS,c),this.$trigger=a('[data-toggle="collapse"][href="#'+b.id+'"],[data-toggle="collapse"][data-target="#'+b.id+'"]'),this.transitioning=null,this.options.parent?this.$parent=this.getParent():this.addAriaAndCollapsedClass(this.$element,this.$trigger),this.options.toggle&&this.toggle()};d.VERSION="3.3.5",d.TRANSITION_DURATION=350,d.DEFAULTS={toggle:!0},d.prototype.dimension=function(){var a=this.$element.hasClass("width");return a?"width":"height"},d.prototype.show=function(){if(!this.transitioning&&!this.$element.hasClass("in")){var b,e=this.$parent&&this.$parent.children(".panel").children(".in, .collapsing");if(!(e&&e.length&&(b=e.data("bs.collapse"),b&&b.transitioning))){var f=a.Event("show.bs.collapse");if(this.$element.trigger(f),!f.isDefaultPrevented()){e&&e.length&&(c.call(e,"hide"),b||e.data("bs.collapse",null));var g=this.dimension();this.$element.removeClass("collapse").addClass("collapsing")[g](0).attr("aria-expanded",!0),this.$trigger.removeClass("collapsed").attr("aria-expanded",!0),this.transitioning=1;var h=function(){this.$element.removeClass("collapsing").addClass("collapse in")[g](""),this.transitioning=0,this.$element.trigger("shown.bs.collapse")};if(!a.support.transition)return h.call(this);var i=a.camelCase(["scroll",g].join("-"));this.$element.one("bsTransitionEnd",a.proxy(h,this)).emulateTransitionEnd(d.TRANSITION_DURATION)[g](this.$element[0][i])}}}},d.prototype.hide=function(){if(!this.transitioning&&this.$element.hasClass("in")){var b=a.Event("hide.bs.collapse");if(this.$element.trigger(b),!b.isDefaultPrevented()){var c=this.dimension();this.$element[c](this.$element[c]())[0].offsetHeight,this.$element.addClass("collapsing").removeClass("collapse in").attr("aria-expanded",!1),this.$trigger.addClass("collapsed").attr("aria-expanded",!1),this.transitioning=1;var e=function(){this.transitioning=0,this.$element.removeClass("collapsing").addClass("collapse").trigger("hidden.bs.collapse")};return a.support.transition?void this.$element[c](0).one("bsTransitionEnd",a.proxy(e,this)).emulateTransitionEnd(d.TRANSITION_DURATION):e.call(this)}}},d.prototype.toggle=function(){this[this.$element.hasClass("in")?"hide":"show"]()},d.prototype.getParent=function(){return a(this.options.parent).find('[data-toggle="collapse"][data-parent="'+this.options.parent+'"]').each(a.proxy(function(c,d){var e=a(d);this.addAriaAndCollapsedClass(b(e),e)},this)).end()},d.prototype.addAriaAndCollapsedClass=function(a,b){var c=a.hasClass("in");a.attr("aria-expanded",c),b.toggleClass("collapsed",!c).attr("aria-expanded",c)};var e=a.fn.collapse;a.fn.collapse=c,a.fn.collapse.Constructor=d,a.fn.collapse.noConflict=function(){return a.fn.collapse=e,this},a(document).on("click.bs.collapse.data-api",'[data-toggle="collapse"]',function(d){var e=a(this);e.attr("data-target")||d.preventDefault();var f=b(e),g=f.data("bs.collapse"),h=g?"toggle":e.data();c.call(f,h)})}(jQuery),+function(a){"use strict";function b(b){var c=b.attr("data-target");c||(c=b.attr("href"),c=c&&/#[A-Za-z]/.test(c)&&c.replace(/.*(?=#[^\s]*$)/,""));var d=c&&a(c);return d&&d.length?d:b.parent()}function c(c){c&&3===c.which||(a(e).remove(),a(f).each(function(){var d=a(this),e=b(d),f={relatedTarget:this};e.hasClass("open")&&(c&&"click"==c.type&&/input|textarea/i.test(c.target.tagName)&&a.contains(e[0],c.target)||(e.trigger(c=a.Event("hide.bs.dropdown",f)),c.isDefaultPrevented()||(d.attr("aria-expanded","false"),e.removeClass("open").trigger("hidden.bs.dropdown",f))))}))}function d(b){return this.each(function(){var c=a(this),d=c.data("bs.dropdown");d||c.data("bs.dropdown",d=new g(this)),"string"==typeof b&&d[b].call(c)})}var e=".dropdown-backdrop",f='[data-toggle="dropdown"]',g=function(b){a(b).on("click.bs.dropdown",this.toggle)};g.VERSION="3.3.5",g.prototype.toggle=function(d){var e=a(this);if(!e.is(".disabled, :disabled")){var f=b(e),g=f.hasClass("open");if(c(),!g){"ontouchstart"in document.documentElement&&!f.closest(".navbar-nav").length&&a(document.createElement("div")).addClass("dropdown-backdrop").insertAfter(a(this)).on("click",c);var h={relatedTarget:this};if(f.trigger(d=a.Event("show.bs.dropdown",h)),d.isDefaultPrevented())return;e.trigger("focus").attr("aria-expanded","true"),f.toggleClass("open").trigger("shown.bs.dropdown",h)}return!1}},g.prototype.keydown=function(c){if(/(38|40|27|32)/.test(c.which)&&!/input|textarea/i.test(c.target.tagName)){var d=a(this);if(c.preventDefault(),c.stopPropagation(),!d.is(".disabled, :disabled")){var e=b(d),g=e.hasClass("open");if(!g&&27!=c.which||g&&27==c.which)return 27==c.which&&e.find(f).trigger("focus"),d.trigger("click");var h=" li:not(.disabled):visible a",i=e.find(".dropdown-menu"+h);if(i.length){var j=i.index(c.target);38==c.which&&j>0&&j--,40==c.which&&j<i.length-1&&j++,~j||(j=0),i.eq(j).trigger("focus")}}}};var h=a.fn.dropdown;a.fn.dropdown=d,a.fn.dropdown.Constructor=g,a.fn.dropdown.noConflict=function(){return a.fn.dropdown=h,this},a(document).on("click.bs.dropdown.data-api",c).on("click.bs.dropdown.data-api",".dropdown form",function(a){a.stopPropagation()}).on("click.bs.dropdown.data-api",f,g.prototype.toggle).on("keydown.bs.dropdown.data-api",f,g.prototype.keydown).on("keydown.bs.dropdown.data-api",".dropdown-menu",g.prototype.keydown)}(jQuery),+function(a){"use strict";function b(b,d){return this.each(function(){var e=a(this),f=e.data("bs.modal"),g=a.extend({},c.DEFAULTS,e.data(),"object"==typeof b&&b);f||e.data("bs.modal",f=new c(this,g)),"string"==typeof b?f[b](d):g.show&&f.show(d)})}var c=function(b,c){this.options=c,this.$body=a(document.body),this.$element=a(b),this.$dialog=this.$element.find(".modal-dialog"),this.$backdrop=null,this.isShown=null,this.originalBodyPad=null,this.scrollbarWidth=0,this.ignoreBackdropClick=!1,this.options.remote&&this.$element.find(".modal-content").load(this.options.remote,a.proxy(function(){this.$element.trigger("loaded.bs.modal")},this))};c.VERSION="3.3.5",c.TRANSITION_DURATION=300,c.BACKDROP_TRANSITION_DURATION=150,c.DEFAULTS={backdrop:!0,keyboard:!0,show:!0},c.prototype.toggle=function(a){return this.isShown?this.hide():this.show(a)},c.prototype.show=function(b){var d=this,e=a.Event("show.bs.modal",{relatedTarget:b});this.$element.trigger(e),this.isShown||e.isDefaultPrevented()||(this.isShown=!0,this.checkScrollbar(),this.setScrollbar(),this.$body.addClass("modal-open"),this.escape(),this.resize(),this.$element.on("click.dismiss.bs.modal",'[data-dismiss="modal"]',a.proxy(this.hide,this)),this.$dialog.on("mousedown.dismiss.bs.modal",function(){d.$element.one("mouseup.dismiss.bs.modal",function(b){a(b.target).is(d.$element)&&(d.ignoreBackdropClick=!0)})}),this.backdrop(function(){var e=a.support.transition&&d.$element.hasClass("fade");d.$element.parent().length||d.$element.appendTo(d.$body),d.$element.show().scrollTop(0),d.adjustDialog(),e&&d.$element[0].offsetWidth,d.$element.addClass("in"),d.enforceFocus();var f=a.Event("shown.bs.modal",{relatedTarget:b});e?d.$dialog.one("bsTransitionEnd",function(){d.$element.trigger("focus").trigger(f)}).emulateTransitionEnd(c.TRANSITION_DURATION):d.$element.trigger("focus").trigger(f)}))},c.prototype.hide=function(b){b&&b.preventDefault(),b=a.Event("hide.bs.modal"),this.$element.trigger(b),this.isShown&&!b.isDefaultPrevented()&&(this.isShown=!1,this.escape(),this.resize(),a(document).off("focusin.bs.modal"),this.$element.removeClass("in").off("click.dismiss.bs.modal").off("mouseup.dismiss.bs.modal"),this.$dialog.off("mousedown.dismiss.bs.modal"),a.support.transition&&this.$element.hasClass("fade")?this.$element.one("bsTransitionEnd",a.proxy(this.hideModal,this)).emulateTransitionEnd(c.TRANSITION_DURATION):this.hideModal())},c.prototype.enforceFocus=function(){a(document).off("focusin.bs.modal").on("focusin.bs.modal",a.proxy(function(a){this.$element[0]===a.target||this.$element.has(a.target).length||this.$element.trigger("focus")},this))},c.prototype.escape=function(){this.isShown&&this.options.keyboard?this.$element.on("keydown.dismiss.bs.modal",a.proxy(function(a){27==a.which&&this.hide()},this)):this.isShown||this.$element.off("keydown.dismiss.bs.modal")},c.prototype.resize=function(){this.isShown?a(window).on("resize.bs.modal",a.proxy(this.handleUpdate,this)):a(window).off("resize.bs.modal")},c.prototype.hideModal=function(){var a=this;this.$element.hide(),this.backdrop(function(){a.$body.removeClass("modal-open"),a.resetAdjustments(),a.resetScrollbar(),a.$element.trigger("hidden.bs.modal")})},c.prototype.removeBackdrop=function(){this.$backdrop&&this.$backdrop.remove(),this.$backdrop=null},c.prototype.backdrop=function(b){var d=this,e=this.$element.hasClass("fade")?"fade":"";if(this.isShown&&this.options.backdrop){var f=a.support.transition&&e;if(this.$backdrop=a(document.createElement("div")).addClass("modal-backdrop "+e).appendTo(this.$body),this.$element.on("click.dismiss.bs.modal",a.proxy(function(a){return this.ignoreBackdropClick?void(this.ignoreBackdropClick=!1):void(a.target===a.currentTarget&&("static"==this.options.backdrop?this.$element[0].focus():this.hide()))},this)),f&&this.$backdrop[0].offsetWidth,this.$backdrop.addClass("in"),!b)return;f?this.$backdrop.one("bsTransitionEnd",b).emulateTransitionEnd(c.BACKDROP_TRANSITION_DURATION):b()}else if(!this.isShown&&this.$backdrop){this.$backdrop.removeClass("in");var g=function(){d.removeBackdrop(),b&&b()};a.support.transition&&this.$element.hasClass("fade")?this.$backdrop.one("bsTransitionEnd",g).emulateTransitionEnd(c.BACKDROP_TRANSITION_DURATION):g()}else b&&b()},c.prototype.handleUpdate=function(){this.adjustDialog()},c.prototype.adjustDialog=function(){var a=this.$element[0].scrollHeight>document.documentElement.clientHeight;this.$element.css({paddingLeft:!this.bodyIsOverflowing&&a?this.scrollbarWidth:"",paddingRight:this.bodyIsOverflowing&&!a?this.scrollbarWidth:""})},c.prototype.resetAdjustments=function(){this.$element.css({paddingLeft:"",paddingRight:""})},c.prototype.checkScrollbar=function(){var a=window.innerWidth;if(!a){var b=document.documentElement.getBoundingClientRect();a=b.right-Math.abs(b.left)}this.bodyIsOverflowing=document.body.clientWidth<a,this.scrollbarWidth=this.measureScrollbar()},c.prototype.setScrollbar=function(){var a=parseInt(this.$body.css("padding-right")||0,10);this.originalBodyPad=document.body.style.paddingRight||"",this.bodyIsOverflowing&&this.$body.css("padding-right",a+this.scrollbarWidth)},c.prototype.resetScrollbar=function(){this.$body.css("padding-right",this.originalBodyPad)},c.prototype.measureScrollbar=function(){var a=document.createElement("div");a.className="modal-scrollbar-measure",this.$body.append(a);var b=a.offsetWidth-a.clientWidth;return this.$body[0].removeChild(a),b};var d=a.fn.modal;a.fn.modal=b,a.fn.modal.Constructor=c,a.fn.modal.noConflict=function(){return a.fn.modal=d,this},a(document).on("click.bs.modal.data-api",'[data-toggle="modal"]',function(c){var d=a(this),e=d.attr("href"),f=a(d.attr("data-target")||e&&e.replace(/.*(?=#[^\s]+$)/,"")),g=f.data("bs.modal")?"toggle":a.extend({remote:!/#/.test(e)&&e},f.data(),d.data());d.is("a")&&c.preventDefault(),f.one("show.bs.modal",function(a){a.isDefaultPrevented()||f.one("hidden.bs.modal",function(){d.is(":visible")&&d.trigger("focus")})}),b.call(f,g,this)})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.tooltip"),f="object"==typeof b&&b;(e||!/destroy|hide/.test(b))&&(e||d.data("bs.tooltip",e=new c(this,f)),"string"==typeof b&&e[b]())})}var c=function(a,b){this.type=null,this.options=null,this.enabled=null,this.timeout=null,this.hoverState=null,this.$element=null,this.inState=null,this.init("tooltip",a,b)};c.VERSION="3.3.5",c.TRANSITION_DURATION=150,c.DEFAULTS={animation:!0,placement:"top",selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,container:!1,viewport:{selector:"body",padding:0}},c.prototype.init=function(b,c,d){if(this.enabled=!0,this.type=b,this.$element=a(c),this.options=this.getOptions(d),this.$viewport=this.options.viewport&&a(a.isFunction(this.options.viewport)?this.options.viewport.call(this,this.$element):this.options.viewport.selector||this.options.viewport),this.inState={click:!1,hover:!1,focus:!1},this.$element[0]instanceof document.constructor&&!this.options.selector)throw new Error("`selector` option must be specified when initializing "+this.type+" on the window.document object!");for(var e=this.options.trigger.split(" "),f=e.length;f--;){var g=e[f];if("click"==g)this.$element.on("click."+this.type,this.options.selector,a.proxy(this.toggle,this));else if("manual"!=g){var h="hover"==g?"mouseenter":"focusin",i="hover"==g?"mouseleave":"focusout";this.$element.on(h+"."+this.type,this.options.selector,a.proxy(this.enter,this)),this.$element.on(i+"."+this.type,this.options.selector,a.proxy(this.leave,this))}}this.options.selector?this._options=a.extend({},this.options,{trigger:"manual",selector:""}):this.fixTitle()},c.prototype.getDefaults=function(){return c.DEFAULTS},c.prototype.getOptions=function(b){return b=a.extend({},this.getDefaults(),this.$element.data(),b),b.delay&&"number"==typeof b.delay&&(b.delay={show:b.delay,hide:b.delay}),b},c.prototype.getDelegateOptions=function(){var b={},c=this.getDefaults();return this._options&&a.each(this._options,function(a,d){c[a]!=d&&(b[a]=d)}),b},c.prototype.enter=function(b){var c=b instanceof this.constructor?b:a(b.currentTarget).data("bs."+this.type);return c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c)),b instanceof a.Event&&(c.inState["focusin"==b.type?"focus":"hover"]=!0),c.tip().hasClass("in")||"in"==c.hoverState?void(c.hoverState="in"):(clearTimeout(c.timeout),c.hoverState="in",c.options.delay&&c.options.delay.show?void(c.timeout=setTimeout(function(){"in"==c.hoverState&&c.show()},c.options.delay.show)):c.show())},c.prototype.isInStateTrue=function(){for(var a in this.inState)if(this.inState[a])return!0;return!1},c.prototype.leave=function(b){var c=b instanceof this.constructor?b:a(b.currentTarget).data("bs."+this.type);return c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c)),b instanceof a.Event&&(c.inState["focusout"==b.type?"focus":"hover"]=!1),c.isInStateTrue()?void 0:(clearTimeout(c.timeout),c.hoverState="out",c.options.delay&&c.options.delay.hide?void(c.timeout=setTimeout(function(){"out"==c.hoverState&&c.hide()},c.options.delay.hide)):c.hide())},c.prototype.show=function(){var b=a.Event("show.bs."+this.type);if(this.hasContent()&&this.enabled){this.$element.trigger(b);var d=a.contains(this.$element[0].ownerDocument.documentElement,this.$element[0]);if(b.isDefaultPrevented()||!d)return;var e=this,f=this.tip(),g=this.getUID(this.type);this.setContent(),f.attr("id",g),this.$element.attr("aria-describedby",g),this.options.animation&&f.addClass("fade");var h="function"==typeof this.options.placement?this.options.placement.call(this,f[0],this.$element[0]):this.options.placement,i=/\s?auto?\s?/i,j=i.test(h);j&&(h=h.replace(i,"")||"top"),f.detach().css({top:0,left:0,display:"block"}).addClass(h).data("bs."+this.type,this),this.options.container?f.appendTo(this.options.container):f.insertAfter(this.$element),this.$element.trigger("inserted.bs."+this.type);var k=this.getPosition(),l=f[0].offsetWidth,m=f[0].offsetHeight;if(j){var n=h,o=this.getPosition(this.$viewport);h="bottom"==h&&k.bottom+m>o.bottom?"top":"top"==h&&k.top-m<o.top?"bottom":"right"==h&&k.right+l>o.width?"left":"left"==h&&k.left-l<o.left?"right":h,f.removeClass(n).addClass(h)}var p=this.getCalculatedOffset(h,k,l,m);this.applyPlacement(p,h);var q=function(){var a=e.hoverState;e.$element.trigger("shown.bs."+e.type),e.hoverState=null,"out"==a&&e.leave(e)};a.support.transition&&this.$tip.hasClass("fade")?f.one("bsTransitionEnd",q).emulateTransitionEnd(c.TRANSITION_DURATION):q()}},c.prototype.applyPlacement=function(b,c){var d=this.tip(),e=d[0].offsetWidth,f=d[0].offsetHeight,g=parseInt(d.css("margin-top"),10),h=parseInt(d.css("margin-left"),10);isNaN(g)&&(g=0),isNaN(h)&&(h=0),b.top+=g,b.left+=h,a.offset.setOffset(d[0],a.extend({using:function(a){d.css({top:Math.round(a.top),left:Math.round(a.left)})}},b),0),d.addClass("in");var i=d[0].offsetWidth,j=d[0].offsetHeight;"top"==c&&j!=f&&(b.top=b.top+f-j);var k=this.getViewportAdjustedDelta(c,b,i,j);k.left?b.left+=k.left:b.top+=k.top;var l=/top|bottom/.test(c),m=l?2*k.left-e+i:2*k.top-f+j,n=l?"offsetWidth":"offsetHeight";d.offset(b),this.replaceArrow(m,d[0][n],l)},c.prototype.replaceArrow=function(a,b,c){this.arrow().css(c?"left":"top",50*(1-a/b)+"%").css(c?"top":"left","")},c.prototype.setContent=function(){var a=this.tip(),b=this.getTitle();a.find(".tooltip-inner")[this.options.html?"html":"text"](b),a.removeClass("fade in top bottom left right")},c.prototype.hide=function(b){function d(){"in"!=e.hoverState&&f.detach(),e.$element.removeAttr("aria-describedby").trigger("hidden.bs."+e.type),b&&b()}var e=this,f=a(this.$tip),g=a.Event("hide.bs."+this.type);return this.$element.trigger(g),g.isDefaultPrevented()?void 0:(f.removeClass("in"),a.support.transition&&f.hasClass("fade")?f.one("bsTransitionEnd",d).emulateTransitionEnd(c.TRANSITION_DURATION):d(),this.hoverState=null,this)},c.prototype.fixTitle=function(){var a=this.$element;(a.attr("title")||"string"!=typeof a.attr("data-original-title"))&&a.attr("data-original-title",a.attr("title")||"").attr("title","")},c.prototype.hasContent=function(){return this.getTitle()},c.prototype.getPosition=function(b){b=b||this.$element;var c=b[0],d="BODY"==c.tagName,e=c.getBoundingClientRect();null==e.width&&(e=a.extend({},e,{width:e.right-e.left,height:e.bottom-e.top}));var f=d?{top:0,left:0}:b.offset(),g={scroll:d?document.documentElement.scrollTop||document.body.scrollTop:b.scrollTop()},h=d?{width:a(window).width(),height:a(window).height()}:null;return a.extend({},e,g,h,f)},c.prototype.getCalculatedOffset=function(a,b,c,d){return"bottom"==a?{top:b.top+b.height,left:b.left+b.width/2-c/2}:"top"==a?{top:b.top-d,left:b.left+b.width/2-c/2}:"left"==a?{top:b.top+b.height/2-d/2,left:b.left-c}:{top:b.top+b.height/2-d/2,left:b.left+b.width}},c.prototype.getViewportAdjustedDelta=function(a,b,c,d){var e={top:0,left:0};if(!this.$viewport)return e;var f=this.options.viewport&&this.options.viewport.padding||0,g=this.getPosition(this.$viewport);if(/right|left/.test(a)){var h=b.top-f-g.scroll,i=b.top+f-g.scroll+d;h<g.top?e.top=g.top-h:i>g.top+g.height&&(e.top=g.top+g.height-i)}else{var j=b.left-f,k=b.left+f+c;j<g.left?e.left=g.left-j:k>g.right&&(e.left=g.left+g.width-k)}return e},c.prototype.getTitle=function(){var a,b=this.$element,c=this.options;return a=b.attr("data-original-title")||("function"==typeof c.title?c.title.call(b[0]):c.title)},c.prototype.getUID=function(a){do a+=~~(1e6*Math.random());while(document.getElementById(a));return a},c.prototype.tip=function(){if(!this.$tip&&(this.$tip=a(this.options.template),1!=this.$tip.length))throw new Error(this.type+" `template` option must consist of exactly 1 top-level element!");return this.$tip},c.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(".tooltip-arrow")},c.prototype.enable=function(){this.enabled=!0},c.prototype.disable=function(){this.enabled=!1},c.prototype.toggleEnabled=function(){this.enabled=!this.enabled},c.prototype.toggle=function(b){var c=this;b&&(c=a(b.currentTarget).data("bs."+this.type),c||(c=new this.constructor(b.currentTarget,this.getDelegateOptions()),a(b.currentTarget).data("bs."+this.type,c))),b?(c.inState.click=!c.inState.click,c.isInStateTrue()?c.enter(c):c.leave(c)):c.tip().hasClass("in")?c.leave(c):c.enter(c)},c.prototype.destroy=function(){var a=this;clearTimeout(this.timeout),this.hide(function(){a.$element.off("."+a.type).removeData("bs."+a.type),a.$tip&&a.$tip.detach(),a.$tip=null,a.$arrow=null,a.$viewport=null})};var d=a.fn.tooltip;a.fn.tooltip=b,a.fn.tooltip.Constructor=c,a.fn.tooltip.noConflict=function(){return a.fn.tooltip=d,this}}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.popover"),f="object"==typeof b&&b;(e||!/destroy|hide/.test(b))&&(e||d.data("bs.popover",e=new c(this,f)),"string"==typeof b&&e[b]())})}var c=function(a,b){this.init("popover",a,b)};if(!a.fn.tooltip)throw new Error("Popover requires tooltip.js");c.VERSION="3.3.5",c.DEFAULTS=a.extend({},a.fn.tooltip.Constructor.DEFAULTS,{placement:"right",trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="arrow"></div><h3 class="popover-title"></h3><div class="popover-content"></div></div>'}),c.prototype=a.extend({},a.fn.tooltip.Constructor.prototype),c.prototype.constructor=c,c.prototype.getDefaults=function(){return c.DEFAULTS},c.prototype.setContent=function(){var a=this.tip(),b=this.getTitle(),c=this.getContent();a.find(".popover-title")[this.options.html?"html":"text"](b),a.find(".popover-content").children().detach().end()[this.options.html?"string"==typeof c?"html":"append":"text"](c),a.removeClass("fade top bottom left right in"),a.find(".popover-title").html()||a.find(".popover-title").hide()},c.prototype.hasContent=function(){return this.getTitle()||this.getContent()},c.prototype.getContent=function(){var a=this.$element,b=this.options;return a.attr("data-content")||("function"==typeof b.content?b.content.call(a[0]):b.content)},c.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(".arrow")};var d=a.fn.popover;a.fn.popover=b,a.fn.popover.Constructor=c,a.fn.popover.noConflict=function(){return a.fn.popover=d,this}}(jQuery),+function(a){"use strict";function b(c,d){this.$body=a(document.body),this.$scrollElement=a(a(c).is(document.body)?window:c),this.options=a.extend({},b.DEFAULTS,d),this.selector=(this.options.target||"")+" .nav li > a",this.offsets=[],this.targets=[],this.activeTarget=null,this.scrollHeight=0,this.$scrollElement.on("scroll.bs.scrollspy",a.proxy(this.process,this)),this.refresh(),this.process()}function c(c){return this.each(function(){var d=a(this),e=d.data("bs.scrollspy"),f="object"==typeof c&&c;e||d.data("bs.scrollspy",e=new b(this,f)),"string"==typeof c&&e[c]()})}b.VERSION="3.3.5",b.DEFAULTS={offset:10},b.prototype.getScrollHeight=function(){return this.$scrollElement[0].scrollHeight||Math.max(this.$body[0].scrollHeight,document.documentElement.scrollHeight)},b.prototype.refresh=function(){var b=this,c="offset",d=0;this.offsets=[],this.targets=[],this.scrollHeight=this.getScrollHeight(),a.isWindow(this.$scrollElement[0])||(c="position",d=this.$scrollElement.scrollTop()),this.$body.find(this.selector).map(function(){var b=a(this),e=b.data("target")||b.attr("href"),f=/^#./.test(e)&&a(e);return f&&f.length&&f.is(":visible")&&[[f[c]().top+d,e]]||null}).sort(function(a,b){return a[0]-b[0]}).each(function(){b.offsets.push(this[0]),b.targets.push(this[1])})},b.prototype.process=function(){var a,b=this.$scrollElement.scrollTop()+this.options.offset,c=this.getScrollHeight(),d=this.options.offset+c-this.$scrollElement.height(),e=this.offsets,f=this.targets,g=this.activeTarget;if(this.scrollHeight!=c&&this.refresh(),b>=d)return g!=(a=f[f.length-1])&&this.activate(a);if(g&&b<e[0])return this.activeTarget=null,this.clear();for(a=e.length;a--;)g!=f[a]&&b>=e[a]&&(void 0===e[a+1]||b<e[a+1])&&this.activate(f[a])},b.prototype.activate=function(b){this.activeTarget=b,this.clear();var c=this.selector+'[data-target="'+b+'"],'+this.selector+'[href="'+b+'"]',d=a(c).parents("li").addClass("active");d.parent(".dropdown-menu").length&&(d=d.closest("li.dropdown").addClass("active")),
d.trigger("activate.bs.scrollspy")},b.prototype.clear=function(){a(this.selector).parentsUntil(this.options.target,".active").removeClass("active")};var d=a.fn.scrollspy;a.fn.scrollspy=c,a.fn.scrollspy.Constructor=b,a.fn.scrollspy.noConflict=function(){return a.fn.scrollspy=d,this},a(window).on("load.bs.scrollspy.data-api",function(){a('[data-spy="scroll"]').each(function(){var b=a(this);c.call(b,b.data())})})}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.tab");e||d.data("bs.tab",e=new c(this)),"string"==typeof b&&e[b]()})}var c=function(b){this.element=a(b)};c.VERSION="3.3.5",c.TRANSITION_DURATION=150,c.prototype.show=function(){var b=this.element,c=b.closest("ul:not(.dropdown-menu)"),d=b.data("target");if(d||(d=b.attr("href"),d=d&&d.replace(/.*(?=#[^\s]*$)/,"")),!b.parent("li").hasClass("active")){var e=c.find(".active:last a"),f=a.Event("hide.bs.tab",{relatedTarget:b[0]}),g=a.Event("show.bs.tab",{relatedTarget:e[0]});if(e.trigger(f),b.trigger(g),!g.isDefaultPrevented()&&!f.isDefaultPrevented()){var h=a(d);this.activate(b.closest("li"),c),this.activate(h,h.parent(),function(){e.trigger({type:"hidden.bs.tab",relatedTarget:b[0]}),b.trigger({type:"shown.bs.tab",relatedTarget:e[0]})})}}},c.prototype.activate=function(b,d,e){function f(){g.removeClass("active").find("> .dropdown-menu > .active").removeClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!1),b.addClass("active").find('[data-toggle="tab"]').attr("aria-expanded",!0),h?(b[0].offsetWidth,b.addClass("in")):b.removeClass("fade"),b.parent(".dropdown-menu").length&&b.closest("li.dropdown").addClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!0),e&&e()}var g=d.find("> .active"),h=e&&a.support.transition&&(g.length&&g.hasClass("fade")||!!d.find("> .fade").length);g.length&&h?g.one("bsTransitionEnd",f).emulateTransitionEnd(c.TRANSITION_DURATION):f(),g.removeClass("in")};var d=a.fn.tab;a.fn.tab=b,a.fn.tab.Constructor=c,a.fn.tab.noConflict=function(){return a.fn.tab=d,this};var e=function(c){c.preventDefault(),b.call(a(this),"show")};a(document).on("click.bs.tab.data-api",'[data-toggle="tab"]',e).on("click.bs.tab.data-api",'[data-toggle="pill"]',e)}(jQuery),+function(a){"use strict";function b(b){return this.each(function(){var d=a(this),e=d.data("bs.affix"),f="object"==typeof b&&b;e||d.data("bs.affix",e=new c(this,f)),"string"==typeof b&&e[b]()})}var c=function(b,d){this.options=a.extend({},c.DEFAULTS,d),this.$target=a(this.options.target).on("scroll.bs.affix.data-api",a.proxy(this.checkPosition,this)).on("click.bs.affix.data-api",a.proxy(this.checkPositionWithEventLoop,this)),this.$element=a(b),this.affixed=null,this.unpin=null,this.pinnedOffset=null,this.checkPosition()};c.VERSION="3.3.5",c.RESET="affix affix-top affix-bottom",c.DEFAULTS={offset:0,target:window},c.prototype.getState=function(a,b,c,d){var e=this.$target.scrollTop(),f=this.$element.offset(),g=this.$target.height();if(null!=c&&"top"==this.affixed)return c>e?"top":!1;if("bottom"==this.affixed)return null!=c?e+this.unpin<=f.top?!1:"bottom":a-d>=e+g?!1:"bottom";var h=null==this.affixed,i=h?e:f.top,j=h?g:b;return null!=c&&c>=e?"top":null!=d&&i+j>=a-d?"bottom":!1},c.prototype.getPinnedOffset=function(){if(this.pinnedOffset)return this.pinnedOffset;this.$element.removeClass(c.RESET).addClass("affix");var a=this.$target.scrollTop(),b=this.$element.offset();return this.pinnedOffset=b.top-a},c.prototype.checkPositionWithEventLoop=function(){setTimeout(a.proxy(this.checkPosition,this),1)},c.prototype.checkPosition=function(){if(this.$element.is(":visible")){var b=this.$element.height(),d=this.options.offset,e=d.top,f=d.bottom,g=Math.max(a(document).height(),a(document.body).height());"object"!=typeof d&&(f=e=d),"function"==typeof e&&(e=d.top(this.$element)),"function"==typeof f&&(f=d.bottom(this.$element));var h=this.getState(g,b,e,f);if(this.affixed!=h){null!=this.unpin&&this.$element.css("top","");var i="affix"+(h?"-"+h:""),j=a.Event(i+".bs.affix");if(this.$element.trigger(j),j.isDefaultPrevented())return;this.affixed=h,this.unpin="bottom"==h?this.getPinnedOffset():null,this.$element.removeClass(c.RESET).addClass(i).trigger(i.replace("affix","affixed")+".bs.affix")}"bottom"==h&&this.$element.offset({top:g-b-f})}};var d=a.fn.affix;a.fn.affix=b,a.fn.affix.Constructor=c,a.fn.affix.noConflict=function(){return a.fn.affix=d,this},a(window).on("load",function(){a('[data-spy="affix"]').each(function(){var c=a(this),d=c.data();d.offset=d.offset||{},null!=d.offsetBottom&&(d.offset.bottom=d.offsetBottom),null!=d.offsetTop&&(d.offset.top=d.offsetTop),b.call(c,d)})})}(jQuery);</script>
<script>/**
* @preserve HTML5 Shiv 3.7.2 | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
*/
// Only run this code in IE 8
if (!!window.navigator.userAgent.match("MSIE 8")) {
!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x<style>"+b+"</style>",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=t.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=t.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),t.elements=c+" "+a,j(b)}function f(a){var b=s[a[q]];return b||(b={},r++,a[q]=r,s[r]=b),b}function g(a,c,d){if(c||(c=b),l)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():p.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||o.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),l)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return t.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(t,b.frag)}function j(a){a||(a=b);var d=f(a);return!t.shivCSS||k||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),l||i(a,d),a}var k,l,m="3.7.2",n=a.html5||{},o=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,p=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,q="_html5shiv",r=0,s={};!function(){try{var a=b.createElement("a");a.innerHTML="<xyz></xyz>",k="hidden"in a,l=1==a.childNodes.length||function(){b.createElement("a");var a=b.createDocumentFragment();return"undefined"==typeof a.cloneNode||"undefined"==typeof a.createDocumentFragment||"undefined"==typeof a.createElement}()}catch(c){k=!0,l=!0}}();var t={elements:n.elements||"abbr article aside audio bdi canvas data datalist details dialog figcaption figure footer header hgroup main mark meter nav output picture progress section summary template time video",version:m,shivCSS:n.shivCSS!==!1,supportsUnknownElements:l,shivMethods:n.shivMethods!==!1,type:"default",shivDocument:j,createElement:g,createDocumentFragment:h,addElements:e};a.html5=t,j(b)}(this,document);
};
</script>
<script>/*! Respond.js v1.4.2: min/max-width media query polyfill * Copyright 2013 Scott Jehl
 * Licensed under https://github.com/scottjehl/Respond/blob/master/LICENSE-MIT
 *  */

// Only run this code in IE 8
if (!!window.navigator.userAgent.match("MSIE 8")) {
!function(a){"use strict";a.matchMedia=a.matchMedia||function(a){var b,c=a.documentElement,d=c.firstElementChild||c.firstChild,e=a.createElement("body"),f=a.createElement("div");return f.id="mq-test-1",f.style.cssText="position:absolute;top:-100em",e.style.background="none",e.appendChild(f),function(a){return f.innerHTML='&shy;<style media="'+a+'"> #mq-test-1 { width: 42px; }</style>',c.insertBefore(e,d),b=42===f.offsetWidth,c.removeChild(e),{matches:b,media:a}}}(a.document)}(this),function(a){"use strict";function b(){u(!0)}var c={};a.respond=c,c.update=function(){};var d=[],e=function(){var b=!1;try{b=new a.XMLHttpRequest}catch(c){b=new a.ActiveXObject("Microsoft.XMLHTTP")}return function(){return b}}(),f=function(a,b){var c=e();c&&(c.open("GET",a,!0),c.onreadystatechange=function(){4!==c.readyState||200!==c.status&&304!==c.status||b(c.responseText)},4!==c.readyState&&c.send(null))};if(c.ajax=f,c.queue=d,c.regex={media:/@media[^\{]+\{([^\{\}]*\{[^\}\{]*\})+/gi,keyframes:/@(?:\-(?:o|moz|webkit)\-)?keyframes[^\{]+\{(?:[^\{\}]*\{[^\}\{]*\})+[^\}]*\}/gi,urls:/(url\()['"]?([^\/\)'"][^:\)'"]+)['"]?(\))/g,findStyles:/@media *([^\{]+)\{([\S\s]+?)$/,only:/(only\s+)?([a-zA-Z]+)\s?/,minw:/\([\s]*min\-width\s*:[\s]*([\s]*[0-9\.]+)(px|em)[\s]*\)/,maxw:/\([\s]*max\-width\s*:[\s]*([\s]*[0-9\.]+)(px|em)[\s]*\)/},c.mediaQueriesSupported=a.matchMedia&&null!==a.matchMedia("only all")&&a.matchMedia("only all").matches,!c.mediaQueriesSupported){var g,h,i,j=a.document,k=j.documentElement,l=[],m=[],n=[],o={},p=30,q=j.getElementsByTagName("head")[0]||k,r=j.getElementsByTagName("base")[0],s=q.getElementsByTagName("link"),t=function(){var a,b=j.createElement("div"),c=j.body,d=k.style.fontSize,e=c&&c.style.fontSize,f=!1;return b.style.cssText="position:absolute;font-size:1em;width:1em",c||(c=f=j.createElement("body"),c.style.background="none"),k.style.fontSize="100%",c.style.fontSize="100%",c.appendChild(b),f&&k.insertBefore(c,k.firstChild),a=b.offsetWidth,f?k.removeChild(c):c.removeChild(b),k.style.fontSize=d,e&&(c.style.fontSize=e),a=i=parseFloat(a)},u=function(b){var c="clientWidth",d=k[c],e="CSS1Compat"===j.compatMode&&d||j.body[c]||d,f={},o=s[s.length-1],r=(new Date).getTime();if(b&&g&&p>r-g)return a.clearTimeout(h),h=a.setTimeout(u,p),void 0;g=r;for(var v in l)if(l.hasOwnProperty(v)){var w=l[v],x=w.minw,y=w.maxw,z=null===x,A=null===y,B="em";x&&(x=parseFloat(x)*(x.indexOf(B)>-1?i||t():1)),y&&(y=parseFloat(y)*(y.indexOf(B)>-1?i||t():1)),w.hasquery&&(z&&A||!(z||e>=x)||!(A||y>=e))||(f[w.media]||(f[w.media]=[]),f[w.media].push(m[w.rules]))}for(var C in n)n.hasOwnProperty(C)&&n[C]&&n[C].parentNode===q&&q.removeChild(n[C]);n.length=0;for(var D in f)if(f.hasOwnProperty(D)){var E=j.createElement("style"),F=f[D].join("\n");E.type="text/css",E.media=D,q.insertBefore(E,o.nextSibling),E.styleSheet?E.styleSheet.cssText=F:E.appendChild(j.createTextNode(F)),n.push(E)}},v=function(a,b,d){var e=a.replace(c.regex.keyframes,"").match(c.regex.media),f=e&&e.length||0;b=b.substring(0,b.lastIndexOf("/"));var g=function(a){return a.replace(c.regex.urls,"$1"+b+"$2$3")},h=!f&&d;b.length&&(b+="/"),h&&(f=1);for(var i=0;f>i;i++){var j,k,n,o;h?(j=d,m.push(g(a))):(j=e[i].match(c.regex.findStyles)&&RegExp.$1,m.push(RegExp.$2&&g(RegExp.$2))),n=j.split(","),o=n.length;for(var p=0;o>p;p++)k=n[p],l.push({media:k.split("(")[0].match(c.regex.only)&&RegExp.$2||"all",rules:m.length-1,hasquery:k.indexOf("(")>-1,minw:k.match(c.regex.minw)&&parseFloat(RegExp.$1)+(RegExp.$2||""),maxw:k.match(c.regex.maxw)&&parseFloat(RegExp.$1)+(RegExp.$2||"")})}u()},w=function(){if(d.length){var b=d.shift();f(b.href,function(c){v(c,b.href,b.media),o[b.href]=!0,a.setTimeout(function(){w()},0)})}},x=function(){for(var b=0;b<s.length;b++){var c=s[b],e=c.href,f=c.media,g=c.rel&&"stylesheet"===c.rel.toLowerCase();e&&g&&!o[e]&&(c.styleSheet&&c.styleSheet.rawCssText?(v(c.styleSheet.rawCssText,e,f),o[e]=!0):(!/^([a-zA-Z:]*\/\/)/.test(e)&&!r||e.replace(RegExp.$1,"").split("/")[0]===a.location.host)&&("//"===e.substring(0,2)&&(e=a.location.protocol+e),d.push({href:e,media:f})))}w()};x(),c.update=x,c.getEmValue=t,a.addEventListener?a.addEventListener("resize",b,!1):a.attachEvent&&a.attachEvent("onresize",b)}}(this);
};
</script>
<style>h1 {font-size: 34px;}
h1.title {font-size: 38px;}
h2 {font-size: 30px;}
h3 {font-size: 24px;}
h4 {font-size: 18px;}
h5 {font-size: 16px;}
h6 {font-size: 12px;}
code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
pre:not([class]) { background-color: white }</style>
<script>

/**
 * jQuery Plugin: Sticky Tabs
 *
 * @author Aidan Lister <aidan@php.net>
 * adapted by Ruben Arslan to activate parent tabs too
 * http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/
 */
(function($) {
  "use strict";
  $.fn.rmarkdownStickyTabs = function() {
    var context = this;
    // Show the tab corresponding with the hash in the URL, or the first tab
    var showStuffFromHash = function() {
      var hash = window.location.hash;
      var selector = hash ? 'a[href="' + hash + '"]' : 'li.active > a';
      var $selector = $(selector, context);
      if($selector.data('toggle') === "tab") {
        $selector.tab('show');
        // walk up the ancestors of this element, show any hidden tabs
        $selector.parents('.section.tabset').each(function(i, elm) {
          var link = $('a[href="#' + $(elm).attr('id') + '"]');
          if(link.data('toggle') === "tab") {
            link.tab("show");
          }
        });
      }
    };


    // Set the correct tab when the page loads
    showStuffFromHash(context);

    // Set the correct tab when a user uses their back/forward button
    $(window).on('hashchange', function() {
      showStuffFromHash(context);
    });

    // Change the URL when tabs are clicked
    $('a', context).on('click', function(e) {
      history.pushState(null, null, this.href);
      showStuffFromHash(context);
    });

    return this;
  };
}(jQuery));

window.buildTabsets = function(tocID) {

  // build a tabset from a section div with the .tabset class
  function buildTabset(tabset) {

    // check for fade and pills options
    var fade = tabset.hasClass("tabset-fade");
    var pills = tabset.hasClass("tabset-pills");
    var navClass = pills ? "nav-pills" : "nav-tabs";

    // determine the heading level of the tabset and tabs
    var match = tabset.attr('class').match(/level(\d) /);
    if (match === null)
      return;
    var tabsetLevel = Number(match[1]);
    var tabLevel = tabsetLevel + 1;

    // find all subheadings immediately below
    var tabs = tabset.find("div.section.level" + tabLevel);
    if (!tabs.length)
      return;

    // create tablist and tab-content elements
    var tabList = $('<ul class="nav ' + navClass + '" role="tablist"></ul>');
    $(tabs[0]).before(tabList);
    var tabContent = $('<div class="tab-content"></div>');
    $(tabs[0]).before(tabContent);

    // build the tabset
    var activeTab = 0;
    tabs.each(function(i) {

      // get the tab div
      var tab = $(tabs[i]);

      // get the id then sanitize it for use with bootstrap tabs
      var id = tab.attr('id');

      // see if this is marked as the active tab
      if (tab.hasClass('active'))
        activeTab = i;

      // remove any table of contents entries associated with
      // this ID (since we'll be removing the heading element)
      $("div#" + tocID + " li a[href='#" + id + "']").parent().remove();

      // sanitize the id for use with bootstrap tabs
      id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
      tab.attr('id', id);

      // get the heading element within it, grab it's text, then remove it
      var heading = tab.find('h' + tabLevel + ':first');
      var headingText = heading.html();
      heading.remove();

      // build and append the tab list item
      var a = $('<a role="tab" data-toggle="tab">' + headingText + '</a>');
      a.attr('href', '#' + id);
      a.attr('aria-controls', id);
      var li = $('<li role="presentation"></li>');
      li.append(a);
      tabList.append(li);

      // set it's attributes
      tab.attr('role', 'tabpanel');
      tab.addClass('tab-pane');
      tab.addClass('tabbed-pane');
      if (fade)
        tab.addClass('fade');

      // move it into the tab content div
      tab.detach().appendTo(tabContent);
    });

    // set active tab
    $(tabList.children('li')[activeTab]).addClass('active');
    var active = $(tabContent.children('div.section')[activeTab]);
    active.addClass('active');
    if (fade)
      active.addClass('in');

    if (tabset.hasClass("tabset-sticky"))
      tabset.rmarkdownStickyTabs();
  }

  // convert section divs with the .tabset class to tabsets
  var tabsets = $("div.section.tabset");
  tabsets.each(function(i) {
    buildTabset($(tabsets[i]));
  });
};

</script>
<style type="text/css">.hljs-literal {
color: #990073;
}
.hljs-number {
color: #099;
}
.hljs-comment {
color: #998;
font-style: italic;
}
.hljs-keyword {
color: #900;
font-weight: bold;
}
.hljs-string {
color: #d14;
}
</style>
<script src="data:application/javascript;base64,/*! highlight.js v9.12.0 | BSD3 License | git.io/hljslicense */
!function(e){var n="object"==typeof window&&window||"object"==typeof self&&self;"undefined"!=typeof exports?e(exports):n&&(n.hljs=e({}),"function"==typeof define&&define.amd&&define([],function(){return n.hljs}))}(function(e){function n(e){return e.replace(/&/g,"&amp;").replace(/</g,"&lt;").replace(/>/g,"&gt;")}function t(e){return e.nodeName.toLowerCase()}function r(e,n){var t=e&&e.exec(n);return t&&0===t.index}function a(e){return k.test(e)}function i(e){var n,t,r,i,o=e.className+" ";if(o+=e.parentNode?e.parentNode.className:"",t=B.exec(o))return w(t[1])?t[1]:"no-highlight";for(o=o.split(/\s+/),n=0,r=o.length;r>n;n++)if(i=o[n],a(i)||w(i))return i}function o(e){var n,t={},r=Array.prototype.slice.call(arguments,1);for(n in e)t[n]=e[n];return r.forEach(function(e){for(n in e)t[n]=e[n]}),t}function u(e){var n=[];return function r(e,a){for(var i=e.firstChild;i;i=i.nextSibling)3===i.nodeType?a+=i.nodeValue.length:1===i.nodeType&&(n.push({event:"start",offset:a,node:i}),a=r(i,a),t(i).match(/br|hr|img|input/)||n.push({event:"stop",offset:a,node:i}));return a}(e,0),n}function c(e,r,a){function i(){return e.length&&r.length?e[0].offset!==r[0].offset?e[0].offset<r[0].offset?e:r:"start"===r[0].event?e:r:e.length?e:r}function o(e){function r(e){return" "+e.nodeName+'="'+n(e.value).replace('"',"&quot;")+'"'}s+="<"+t(e)+E.map.call(e.attributes,r).join("")+">"}function u(e){s+="</"+t(e)+">"}function c(e){("start"===e.event?o:u)(e.node)}for(var l=0,s="",f=[];e.length||r.length;){var g=i();if(s+=n(a.substring(l,g[0].offset)),l=g[0].offset,g===e){f.reverse().forEach(u);do c(g.splice(0,1)[0]),g=i();while(g===e&&g.length&&g[0].offset===l);f.reverse().forEach(o)}else"start"===g[0].event?f.push(g[0].node):f.pop(),c(g.splice(0,1)[0])}return s+n(a.substr(l))}function l(e){return e.v&&!e.cached_variants&&(e.cached_variants=e.v.map(function(n){return o(e,{v:null},n)})),e.cached_variants||e.eW&&[o(e)]||[e]}function s(e){function n(e){return e&&e.source||e}function t(t,r){return new RegExp(n(t),"m"+(e.cI?"i":"")+(r?"g":""))}function r(a,i){if(!a.compiled){if(a.compiled=!0,a.k=a.k||a.bK,a.k){var o={},u=function(n,t){e.cI&&(t=t.toLowerCase()),t.split(" ").forEach(function(e){var t=e.split("|");o[t[0]]=[n,t[1]?Number(t[1]):1]})};"string"==typeof a.k?u("keyword",a.k):x(a.k).forEach(function(e){u(e,a.k[e])}),a.k=o}a.lR=t(a.l||/\w+/,!0),i&&(a.bK&&(a.b="\\b("+a.bK.split(" ").join("|")+")\\b"),a.b||(a.b=/\B|\b/),a.bR=t(a.b),a.e||a.eW||(a.e=/\B|\b/),a.e&&(a.eR=t(a.e)),a.tE=n(a.e)||"",a.eW&&i.tE&&(a.tE+=(a.e?"|":"")+i.tE)),a.i&&(a.iR=t(a.i)),null==a.r&&(a.r=1),a.c||(a.c=[]),a.c=Array.prototype.concat.apply([],a.c.map(function(e){return l("self"===e?a:e)})),a.c.forEach(function(e){r(e,a)}),a.starts&&r(a.starts,i);var c=a.c.map(function(e){return e.bK?"\\.?("+e.b+")\\.?":e.b}).concat([a.tE,a.i]).map(n).filter(Boolean);a.t=c.length?t(c.join("|"),!0):{exec:function(){return null}}}}r(e)}function f(e,t,a,i){function o(e,n){var t,a;for(t=0,a=n.c.length;a>t;t++)if(r(n.c[t].bR,e))return n.c[t]}function u(e,n){if(r(e.eR,n)){for(;e.endsParent&&e.parent;)e=e.parent;return e}return e.eW?u(e.parent,n):void 0}function c(e,n){return!a&&r(n.iR,e)}function l(e,n){var t=N.cI?n[0].toLowerCase():n[0];return e.k.hasOwnProperty(t)&&e.k[t]}function p(e,n,t,r){var a=r?"":I.classPrefix,i='<span class="'+a,o=t?"":C;return i+=e+'">',i+n+o}function h(){var e,t,r,a;if(!E.k)return n(k);for(a="",t=0,E.lR.lastIndex=0,r=E.lR.exec(k);r;)a+=n(k.substring(t,r.index)),e=l(E,r),e?(B+=e[1],a+=p(e[0],n(r[0]))):a+=n(r[0]),t=E.lR.lastIndex,r=E.lR.exec(k);return a+n(k.substr(t))}function d(){var e="string"==typeof E.sL;if(e&&!y[E.sL])return n(k);var t=e?f(E.sL,k,!0,x[E.sL]):g(k,E.sL.length?E.sL:void 0);return E.r>0&&(B+=t.r),e&&(x[E.sL]=t.top),p(t.language,t.value,!1,!0)}function b(){L+=null!=E.sL?d():h(),k=""}function v(e){L+=e.cN?p(e.cN,"",!0):"",E=Object.create(e,{parent:{value:E}})}function m(e,n){if(k+=e,null==n)return b(),0;var t=o(n,E);if(t)return t.skip?k+=n:(t.eB&&(k+=n),b(),t.rB||t.eB||(k=n)),v(t,n),t.rB?0:n.length;var r=u(E,n);if(r){var a=E;a.skip?k+=n:(a.rE||a.eE||(k+=n),b(),a.eE&&(k=n));do E.cN&&(L+=C),E.skip||(B+=E.r),E=E.parent;while(E!==r.parent);return r.starts&&v(r.starts,""),a.rE?0:n.length}if(c(n,E))throw new Error('Illegal lexeme "'+n+'" for mode "'+(E.cN||"<unnamed>")+'"');return k+=n,n.length||1}var N=w(e);if(!N)throw new Error('Unknown language: "'+e+'"');s(N);var R,E=i||N,x={},L="";for(R=E;R!==N;R=R.parent)R.cN&&(L=p(R.cN,"",!0)+L);var k="",B=0;try{for(var M,j,O=0;;){if(E.t.lastIndex=O,M=E.t.exec(t),!M)break;j=m(t.substring(O,M.index),M[0]),O=M.index+j}for(m(t.substr(O)),R=E;R.parent;R=R.parent)R.cN&&(L+=C);return{r:B,value:L,language:e,top:E}}catch(T){if(T.message&&-1!==T.message.indexOf("Illegal"))return{r:0,value:n(t)};throw T}}function g(e,t){t=t||I.languages||x(y);var r={r:0,value:n(e)},a=r;return t.filter(w).forEach(function(n){var t=f(n,e,!1);t.language=n,t.r>a.r&&(a=t),t.r>r.r&&(a=r,r=t)}),a.language&&(r.second_best=a),r}function p(e){return I.tabReplace||I.useBR?e.replace(M,function(e,n){return I.useBR&&"\n"===e?"<br>":I.tabReplace?n.replace(/\t/g,I.tabReplace):""}):e}function h(e,n,t){var r=n?L[n]:t,a=[e.trim()];return e.match(/\bhljs\b/)||a.push("hljs"),-1===e.indexOf(r)&&a.push(r),a.join(" ").trim()}function d(e){var n,t,r,o,l,s=i(e);a(s)||(I.useBR?(n=document.createElementNS("http://www.w3.org/1999/xhtml","div"),n.innerHTML=e.innerHTML.replace(/\n/g,"").replace(/<br[ \/]*>/g,"\n")):n=e,l=n.textContent,r=s?f(s,l,!0):g(l),t=u(n),t.length&&(o=document.createElementNS("http://www.w3.org/1999/xhtml","div"),o.innerHTML=r.value,r.value=c(t,u(o),l)),r.value=p(r.value),e.innerHTML=r.value,e.className=h(e.className,s,r.language),e.result={language:r.language,re:r.r},r.second_best&&(e.second_best={language:r.second_best.language,re:r.second_best.r}))}function b(e){I=o(I,e)}function v(){if(!v.called){v.called=!0;var e=document.querySelectorAll("pre code");E.forEach.call(e,d)}}function m(){addEventListener("DOMContentLoaded",v,!1),addEventListener("load",v,!1)}function N(n,t){var r=y[n]=t(e);r.aliases&&r.aliases.forEach(function(e){L[e]=n})}function R(){return x(y)}function w(e){return e=(e||"").toLowerCase(),y[e]||y[L[e]]}var E=[],x=Object.keys,y={},L={},k=/^(no-?highlight|plain|text)$/i,B=/\blang(?:uage)?-([\w-]+)\b/i,M=/((^(<[^>]+>|\t|)+|(?:\n)))/gm,C="</span>",I={classPrefix:"hljs-",tabReplace:null,useBR:!1,languages:void 0};return e.highlight=f,e.highlightAuto=g,e.fixMarkup=p,e.highlightBlock=d,e.configure=b,e.initHighlighting=v,e.initHighlightingOnLoad=m,e.registerLanguage=N,e.listLanguages=R,e.getLanguage=w,e.inherit=o,e.IR="[a-zA-Z]\\w*",e.UIR="[a-zA-Z_]\\w*",e.NR="\\b\\d+(\\.\\d+)?",e.CNR="(-?)(\\b0[xX][a-fA-F0-9]+|(\\b\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)",e.BNR="\\b(0b[01]+)",e.RSR="!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|-|-=|/=|/|:|;|<<|<<=|<=|<|===|==|=|>>>=|>>=|>=|>>>|>>|>|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~",e.BE={b:"\\\\[\\s\\S]",r:0},e.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[e.BE]},e.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[e.BE]},e.PWM={b:/\b(a|an|the|are|I'm|isn't|don't|doesn't|won't|but|just|should|pretty|simply|enough|gonna|going|wtf|so|such|will|you|your|they|like|more)\b/},e.C=function(n,t,r){var a=e.inherit({cN:"comment",b:n,e:t,c:[]},r||{});return a.c.push(e.PWM),a.c.push({cN:"doctag",b:"(?:TODO|FIXME|NOTE|BUG|XXX):",r:0}),a},e.CLCM=e.C("//","$"),e.CBCM=e.C("/\\*","\\*/"),e.HCM=e.C("#","$"),e.NM={cN:"number",b:e.NR,r:0},e.CNM={cN:"number",b:e.CNR,r:0},e.BNM={cN:"number",b:e.BNR,r:0},e.CSSNM={cN:"number",b:e.NR+"(%|em|ex|ch|rem|vw|vh|vmin|vmax|cm|mm|in|pt|pc|px|deg|grad|rad|turn|s|ms|Hz|kHz|dpi|dpcm|dppx)?",r:0},e.RM={cN:"regexp",b:/\//,e:/\/[gimuy]*/,i:/\n/,c:[e.BE,{b:/\[/,e:/\]/,r:0,c:[e.BE]}]},e.TM={cN:"title",b:e.IR,r:0},e.UTM={cN:"title",b:e.UIR,r:0},e.METHOD_GUARD={b:"\\.\\s*"+e.UIR,r:0},e});hljs.registerLanguage("sql",function(e){var t=e.C("--","$");return{cI:!0,i:/[<>{}*#]/,c:[{bK:"begin end start commit rollback savepoint lock alter create drop rename call delete do handler insert load replace select truncate update set show pragma grant merge describe use explain help declare prepare execute deallocate release unlock purge reset change stop analyze cache flush optimize repair kill install uninstall checksum restore check backup revoke comment",e:/;/,eW:!0,l:/[\w\.]+/,k:{keyword:"abort abs absolute acc acce accep accept access accessed accessible account acos action activate add addtime admin administer advanced advise aes_decrypt aes_encrypt after agent aggregate ali alia alias allocate allow alter always analyze ancillary and any anydata anydataset anyschema anytype apply archive archived archivelog are as asc ascii asin assembly assertion associate asynchronous at atan atn2 attr attri attrib attribu attribut attribute attributes audit authenticated authentication authid authors auto autoallocate autodblink autoextend automatic availability avg backup badfile basicfile before begin beginning benchmark between bfile bfile_base big bigfile bin binary_double binary_float binlog bit_and bit_count bit_length bit_or bit_xor bitmap blob_base block blocksize body both bound buffer_cache buffer_pool build bulk by byte byteordermark bytes cache caching call calling cancel capacity cascade cascaded case cast catalog category ceil ceiling chain change changed char_base char_length character_length characters characterset charindex charset charsetform charsetid check checksum checksum_agg child choose chr chunk class cleanup clear client clob clob_base clone close cluster_id cluster_probability cluster_set clustering coalesce coercibility col collate collation collect colu colum column column_value columns columns_updated comment commit compact compatibility compiled complete composite_limit compound compress compute concat concat_ws concurrent confirm conn connec connect connect_by_iscycle connect_by_isleaf connect_by_root connect_time connection consider consistent constant constraint constraints constructor container content contents context contributors controlfile conv convert convert_tz corr corr_k corr_s corresponding corruption cos cost count count_big counted covar_pop covar_samp cpu_per_call cpu_per_session crc32 create creation critical cross cube cume_dist curdate current current_date current_time current_timestamp current_user cursor curtime customdatum cycle data database databases datafile datafiles datalength date_add date_cache date_format date_sub dateadd datediff datefromparts datename datepart datetime2fromparts day day_to_second dayname dayofmonth dayofweek dayofyear days db_role_change dbtimezone ddl deallocate declare decode decompose decrement decrypt deduplicate def defa defau defaul default defaults deferred defi defin define degrees delayed delegate delete delete_all delimited demand dense_rank depth dequeue des_decrypt des_encrypt des_key_file desc descr descri describ describe descriptor deterministic diagnostics difference dimension direct_load directory disable disable_all disallow disassociate discardfile disconnect diskgroup distinct distinctrow distribute distributed div do document domain dotnet double downgrade drop dumpfile duplicate duration each edition editionable editions element ellipsis else elsif elt empty enable enable_all enclosed encode encoding encrypt end end-exec endian enforced engine engines enqueue enterprise entityescaping eomonth error errors escaped evalname evaluate event eventdata events except exception exceptions exchange exclude excluding execu execut execute exempt exists exit exp expire explain export export_set extended extent external external_1 external_2 externally extract failed failed_login_attempts failover failure far fast feature_set feature_value fetch field fields file file_name_convert filesystem_like_logging final finish first first_value fixed flash_cache flashback floor flush following follows for forall force form forma format found found_rows freelist freelists freepools fresh from from_base64 from_days ftp full function general generated get get_format get_lock getdate getutcdate global global_name globally go goto grant grants greatest group group_concat group_id grouping grouping_id groups gtid_subtract guarantee guard handler hash hashkeys having hea head headi headin heading heap help hex hierarchy high high_priority hosts hour http id ident_current ident_incr ident_seed identified identity idle_time if ifnull ignore iif ilike ilm immediate import in include including increment index indexes indexing indextype indicator indices inet6_aton inet6_ntoa inet_aton inet_ntoa infile initial initialized initially initrans inmemory inner innodb input insert install instance instantiable instr interface interleaved intersect into invalidate invisible is is_free_lock is_ipv4 is_ipv4_compat is_not is_not_null is_used_lock isdate isnull isolation iterate java join json json_exists keep keep_duplicates key keys kill language large last last_day last_insert_id last_value lax lcase lead leading least leaves left len lenght length less level levels library like like2 like4 likec limit lines link list listagg little ln load load_file lob lobs local localtime localtimestamp locate locator lock locked log log10 log2 logfile logfiles logging logical logical_reads_per_call logoff logon logs long loop low low_priority lower lpad lrtrim ltrim main make_set makedate maketime managed management manual map mapping mask master master_pos_wait match matched materialized max maxextents maximize maxinstances maxlen maxlogfiles maxloghistory maxlogmembers maxsize maxtrans md5 measures median medium member memcompress memory merge microsecond mid migration min minextents minimum mining minus minute minvalue missing mod mode model modification modify module monitoring month months mount move movement multiset mutex name name_const names nan national native natural nav nchar nclob nested never new newline next nextval no no_write_to_binlog noarchivelog noaudit nobadfile nocheck nocompress nocopy nocycle nodelay nodiscardfile noentityescaping noguarantee nokeep nologfile nomapping nomaxvalue nominimize nominvalue nomonitoring none noneditionable nonschema noorder nopr nopro noprom nopromp noprompt norely noresetlogs noreverse normal norowdependencies noschemacheck noswitch not nothing notice notrim novalidate now nowait nth_value nullif nulls num numb numbe nvarchar nvarchar2 object ocicoll ocidate ocidatetime ociduration ociinterval ociloblocator ocinumber ociref ocirefcursor ocirowid ocistring ocitype oct octet_length of off offline offset oid oidindex old on online only opaque open operations operator optimal optimize option optionally or oracle oracle_date oradata ord ordaudio orddicom orddoc order ordimage ordinality ordvideo organization orlany orlvary out outer outfile outline output over overflow overriding package pad parallel parallel_enable parameters parent parse partial partition partitions pascal passing password password_grace_time password_lock_time password_reuse_max password_reuse_time password_verify_function patch path patindex pctincrease pctthreshold pctused pctversion percent percent_rank percentile_cont percentile_disc performance period period_add period_diff permanent physical pi pipe pipelined pivot pluggable plugin policy position post_transaction pow power pragma prebuilt precedes preceding precision prediction prediction_cost prediction_details prediction_probability prediction_set prepare present preserve prior priority private private_sga privileges procedural procedure procedure_analyze processlist profiles project prompt protection public publishingservername purge quarter query quick quiesce quota quotename radians raise rand range rank raw read reads readsize rebuild record records recover recovery recursive recycle redo reduced ref reference referenced references referencing refresh regexp_like register regr_avgx regr_avgy regr_count regr_intercept regr_r2 regr_slope regr_sxx regr_sxy reject rekey relational relative relaylog release release_lock relies_on relocate rely rem remainder rename repair repeat replace replicate replication required reset resetlogs resize resource respect restore restricted result result_cache resumable resume retention return returning returns reuse reverse revoke right rlike role roles rollback rolling rollup round row row_count rowdependencies rowid rownum rows rtrim rules safe salt sample save savepoint sb1 sb2 sb4 scan schema schemacheck scn scope scroll sdo_georaster sdo_topo_geometry search sec_to_time second section securefile security seed segment select self sequence sequential serializable server servererror session session_user sessions_per_user set sets settings sha sha1 sha2 share shared shared_pool short show shrink shutdown si_averagecolor si_colorhistogram si_featurelist si_positionalcolor si_stillimage si_texture siblings sid sign sin size size_t sizes skip slave sleep smalldatetimefromparts smallfile snapshot some soname sort soundex source space sparse spfile split sql sql_big_result sql_buffer_result sql_cache sql_calc_found_rows sql_small_result sql_variant_property sqlcode sqldata sqlerror sqlname sqlstate sqrt square standalone standby start starting startup statement static statistics stats_binomial_test stats_crosstab stats_ks_test stats_mode stats_mw_test stats_one_way_anova stats_t_test_ stats_t_test_indep stats_t_test_one stats_t_test_paired stats_wsr_test status std stddev stddev_pop stddev_samp stdev stop storage store stored str str_to_date straight_join strcmp strict string struct stuff style subdate subpartition subpartitions substitutable substr substring subtime subtring_index subtype success sum suspend switch switchoffset switchover sync synchronous synonym sys sys_xmlagg sysasm sysaux sysdate sysdatetimeoffset sysdba sysoper system system_user sysutcdatetime table tables tablespace tan tdo template temporary terminated tertiary_weights test than then thread through tier ties time time_format time_zone timediff timefromparts timeout timestamp timestampadd timestampdiff timezone_abbr timezone_minute timezone_region to to_base64 to_date to_days to_seconds todatetimeoffset trace tracking transaction transactional translate translation treat trigger trigger_nestlevel triggers trim truncate try_cast try_convert try_parse type ub1 ub2 ub4 ucase unarchived unbounded uncompress under undo unhex unicode uniform uninstall union unique unix_timestamp unknown unlimited unlock unpivot unrecoverable unsafe unsigned until untrusted unusable unused update updated upgrade upped upper upsert url urowid usable usage use use_stored_outlines user user_data user_resources users using utc_date utc_timestamp uuid uuid_short validate validate_password_strength validation valist value values var var_samp varcharc vari varia variab variabl variable variables variance varp varraw varrawc varray verify version versions view virtual visible void wait wallet warning warnings week weekday weekofyear wellformed when whene whenev wheneve whenever where while whitespace with within without work wrapped xdb xml xmlagg xmlattributes xmlcast xmlcolattval xmlelement xmlexists xmlforest xmlindex xmlnamespaces xmlpi xmlquery xmlroot xmlschema xmlserialize xmltable xmltype xor year year_to_month years yearweek",literal:"true false null",built_in:"array bigint binary bit blob boolean char character date dec decimal float int int8 integer interval number numeric real record serial serial8 smallint text varchar varying void"},c:[{cN:"string",b:"'",e:"'",c:[e.BE,{b:"''"}]},{cN:"string",b:'"',e:'"',c:[e.BE,{b:'""'}]},{cN:"string",b:"`",e:"`",c:[e.BE]},e.CNM,e.CBCM,t]},e.CBCM,t]}});hljs.registerLanguage("r",function(e){var r="([a-zA-Z]|\\.[a-zA-Z.])[a-zA-Z0-9._]*";return{c:[e.HCM,{b:r,l:r,k:{keyword:"function if in break next repeat else for return switch while try tryCatch stop warning require library attach detach source setMethod setGeneric setGroupGeneric setClass ...",literal:"NULL NA TRUE FALSE T F Inf NaN NA_integer_|10 NA_real_|10 NA_character_|10 NA_complex_|10"},r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"\\d+(?:[eE][+\\-]?\\d*)?L\\b",r:0},{cN:"number",b:"\\d+\\.(?!\\d)(?:i\\b)?",r:0},{cN:"number",b:"\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{b:"`",e:"`",r:0},{cN:"string",c:[e.BE],v:[{b:'"',e:'"'},{b:"'",e:"'"}]}]}});hljs.registerLanguage("perl",function(e){var t="getpwent getservent quotemeta msgrcv scalar kill dbmclose undef lc ma syswrite tr send umask sysopen shmwrite vec qx utime local oct semctl localtime readpipe do return format read sprintf dbmopen pop getpgrp not getpwnam rewinddir qqfileno qw endprotoent wait sethostent bless s|0 opendir continue each sleep endgrent shutdown dump chomp connect getsockname die socketpair close flock exists index shmgetsub for endpwent redo lstat msgctl setpgrp abs exit select print ref gethostbyaddr unshift fcntl syscall goto getnetbyaddr join gmtime symlink semget splice x|0 getpeername recv log setsockopt cos last reverse gethostbyname getgrnam study formline endhostent times chop length gethostent getnetent pack getprotoent getservbyname rand mkdir pos chmod y|0 substr endnetent printf next open msgsnd readdir use unlink getsockopt getpriority rindex wantarray hex system getservbyport endservent int chr untie rmdir prototype tell listen fork shmread ucfirst setprotoent else sysseek link getgrgid shmctl waitpid unpack getnetbyname reset chdir grep split require caller lcfirst until warn while values shift telldir getpwuid my getprotobynumber delete and sort uc defined srand accept package seekdir getprotobyname semop our rename seek if q|0 chroot sysread setpwent no crypt getc chown sqrt write setnetent setpriority foreach tie sin msgget map stat getlogin unless elsif truncate exec keys glob tied closedirioctl socket readlink eval xor readline binmode setservent eof ord bind alarm pipe atan2 getgrent exp time push setgrent gt lt or ne m|0 break given say state when",r={cN:"subst",b:"[$@]\\{",e:"\\}",k:t},s={b:"->{",e:"}"},n={v:[{b:/\$\d/},{b:/[\$%@](\^\w\b|#\w+(::\w+)*|{\w+}|\w+(::\w*)*)/},{b:/[\$%@][^\s\w{]/,r:0}]},i=[e.BE,r,n],o=[n,e.HCM,e.C("^\\=\\w","\\=cut",{eW:!0}),s,{cN:"string",c:i,v:[{b:"q[qwxr]?\\s*\\(",e:"\\)",r:5},{b:"q[qwxr]?\\s*\\[",e:"\\]",r:5},{b:"q[qwxr]?\\s*\\{",e:"\\}",r:5},{b:"q[qwxr]?\\s*\\|",e:"\\|",r:5},{b:"q[qwxr]?\\s*\\<",e:"\\>",r:5},{b:"qw\\s+q",e:"q",r:5},{b:"'",e:"'",c:[e.BE]},{b:'"',e:'"'},{b:"`",e:"`",c:[e.BE]},{b:"{\\w+}",c:[],r:0},{b:"-?\\w+\\s*\\=\\>",c:[],r:0}]},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{b:"(\\/\\/|"+e.RSR+"|\\b(split|return|print|reverse|grep)\\b)\\s*",k:"split return print reverse grep",r:0,c:[e.HCM,{cN:"regexp",b:"(s|tr|y)/(\\\\.|[^/])*/(\\\\.|[^/])*/[a-z]*",r:10},{cN:"regexp",b:"(m|qr)?/",e:"/[a-z]*",c:[e.BE],r:0}]},{cN:"function",bK:"sub",e:"(\\s*\\(.*?\\))?[;{]",eE:!0,r:5,c:[e.TM]},{b:"-\\w\\b",r:0},{b:"^__DATA__$",e:"^__END__$",sL:"mojolicious",c:[{b:"^@@.*",e:"$",cN:"comment"}]}];return r.c=o,s.c=o,{aliases:["pl","pm"],l:/[\w\.]+/,k:t,c:o}});hljs.registerLanguage("ini",function(e){var b={cN:"string",c:[e.BE],v:[{b:"'''",e:"'''",r:10},{b:'"""',e:'"""',r:10},{b:'"',e:'"'},{b:"'",e:"'"}]};return{aliases:["toml"],cI:!0,i:/\S/,c:[e.C(";","$"),e.HCM,{cN:"section",b:/^\s*\[+/,e:/\]+/},{b:/^[a-z0-9\[\]_-]+\s*=\s*/,e:"$",rB:!0,c:[{cN:"attr",b:/[a-z0-9\[\]_-]+/},{b:/=/,eW:!0,r:0,c:[{cN:"literal",b:/\bon|off|true|false|yes|no\b/},{cN:"variable",v:[{b:/\$[\w\d"][\w\d_]*/},{b:/\$\{(.*?)}/}]},b,{cN:"number",b:/([\+\-]+)?[\d]+_[\d_]+/},e.NM]}]}]}});hljs.registerLanguage("diff",function(e){return{aliases:["patch"],c:[{cN:"meta",r:10,v:[{b:/^@@ +\-\d+,\d+ +\+\d+,\d+ +@@$/},{b:/^\*\*\* +\d+,\d+ +\*\*\*\*$/},{b:/^\-\-\- +\d+,\d+ +\-\-\-\-$/}]},{cN:"comment",v:[{b:/Index: /,e:/$/},{b:/={3,}/,e:/$/},{b:/^\-{3}/,e:/$/},{b:/^\*{3} /,e:/$/},{b:/^\+{3}/,e:/$/},{b:/\*{5}/,e:/\*{5}$/}]},{cN:"addition",b:"^\\+",e:"$"},{cN:"deletion",b:"^\\-",e:"$"},{cN:"addition",b:"^\\!",e:"$"}]}});hljs.registerLanguage("go",function(e){var t={keyword:"break default func interface select case map struct chan else goto package switch const fallthrough if range type continue for import return var go defer bool byte complex64 complex128 float32 float64 int8 int16 int32 int64 string uint8 uint16 uint32 uint64 int uint uintptr rune",literal:"true false iota nil",built_in:"append cap close complex copy imag len make new panic print println real recover delete"};return{aliases:["golang"],k:t,i:"</",c:[e.CLCM,e.CBCM,{cN:"string",v:[e.QSM,{b:"'",e:"[^\\\\]'"},{b:"`",e:"`"}]},{cN:"number",v:[{b:e.CNR+"[dflsi]",r:1},e.CNM]},{b:/:=/},{cN:"function",bK:"func",e:/\s*\{/,eE:!0,c:[e.TM,{cN:"params",b:/\(/,e:/\)/,k:t,i:/["']/}]}]}});hljs.registerLanguage("bash",function(e){var t={cN:"variable",v:[{b:/\$[\w\d#@][\w\d_]*/},{b:/\$\{(.*?)}/}]},s={cN:"string",b:/"/,e:/"/,c:[e.BE,t,{cN:"variable",b:/\$\(/,e:/\)/,c:[e.BE]}]},a={cN:"string",b:/'/,e:/'/};return{aliases:["sh","zsh"],l:/\b-?[a-z\._]+\b/,k:{keyword:"if then else elif fi for while in do done case esac function",literal:"true false",built_in:"break cd continue eval exec exit export getopts hash pwd readonly return shift test times trap umask unset alias bind builtin caller command declare echo enable help let local logout mapfile printf read readarray source type typeset ulimit unalias set shopt autoload bg bindkey bye cap chdir clone comparguments compcall compctl compdescribe compfiles compgroups compquote comptags comptry compvalues dirs disable disown echotc echoti emulate fc fg float functions getcap getln history integer jobs kill limit log noglob popd print pushd pushln rehash sched setcap setopt stat suspend ttyctl unfunction unhash unlimit unsetopt vared wait whence where which zcompile zformat zftp zle zmodload zparseopts zprof zpty zregexparse zsocket zstyle ztcp",_:"-ne -eq -lt -gt -f -d -e -s -l -a"},c:[{cN:"meta",b:/^#![^\n]+sh\s*$/,r:10},{cN:"function",b:/\w[\w\d_]*\s*\(\s*\)\s*\{/,rB:!0,c:[e.inherit(e.TM,{b:/\w[\w\d_]*/})],r:0},e.HCM,s,a,t]}});hljs.registerLanguage("python",function(e){var r={keyword:"and elif is global as in if from raise for except finally print import pass return exec else break not with class assert yield try while continue del or def lambda async await nonlocal|10 None True False",built_in:"Ellipsis NotImplemented"},b={cN:"meta",b:/^(>>>|\.\.\.) /},c={cN:"subst",b:/\{/,e:/\}/,k:r,i:/#/},a={cN:"string",c:[e.BE],v:[{b:/(u|b)?r?'''/,e:/'''/,c:[b],r:10},{b:/(u|b)?r?"""/,e:/"""/,c:[b],r:10},{b:/(fr|rf|f)'''/,e:/'''/,c:[b,c]},{b:/(fr|rf|f)"""/,e:/"""/,c:[b,c]},{b:/(u|r|ur)'/,e:/'/,r:10},{b:/(u|r|ur)"/,e:/"/,r:10},{b:/(b|br)'/,e:/'/},{b:/(b|br)"/,e:/"/},{b:/(fr|rf|f)'/,e:/'/,c:[c]},{b:/(fr|rf|f)"/,e:/"/,c:[c]},e.ASM,e.QSM]},s={cN:"number",r:0,v:[{b:e.BNR+"[lLjJ]?"},{b:"\\b(0o[0-7]+)[lLjJ]?"},{b:e.CNR+"[lLjJ]?"}]},i={cN:"params",b:/\(/,e:/\)/,c:["self",b,s,a]};return c.c=[a,s,b],{aliases:["py","gyp"],k:r,i:/(<\/|->|\?)|=>/,c:[b,s,a,e.HCM,{v:[{cN:"function",bK:"def"},{cN:"class",bK:"class"}],e:/:/,i:/[${=;\n,]/,c:[e.UTM,i,{b:/->/,eW:!0,k:"None"}]},{cN:"meta",b:/^[\t ]*@/,e:/$/},{b:/\b(print|exec)\(/}]}});hljs.registerLanguage("julia",function(e){var r={keyword:"in isa where baremodule begin break catch ccall const continue do else elseif end export false finally for function global if import importall let local macro module quote return true try using while type immutable abstract bitstype typealias ",literal:"true false ARGS C_NULL DevNull ENDIAN_BOM ENV I Inf Inf16 Inf32 Inf64 InsertionSort JULIA_HOME LOAD_PATH MergeSort NaN NaN16 NaN32 NaN64 PROGRAM_FILE QuickSort RoundDown RoundFromZero RoundNearest RoundNearestTiesAway RoundNearestTiesUp RoundToZero RoundUp STDERR STDIN STDOUT VERSION catalan e|0 eu|0 eulergamma golden im nothing pi γ π φ ",built_in:"ANY AbstractArray AbstractChannel AbstractFloat AbstractMatrix AbstractRNG AbstractSerializer AbstractSet AbstractSparseArray AbstractSparseMatrix AbstractSparseVector AbstractString AbstractUnitRange AbstractVecOrMat AbstractVector Any ArgumentError Array AssertionError Associative Base64DecodePipe Base64EncodePipe Bidiagonal BigFloat BigInt BitArray BitMatrix BitVector Bool BoundsError BufferStream CachingPool CapturedException CartesianIndex CartesianRange Cchar Cdouble Cfloat Channel Char Cint Cintmax_t Clong Clonglong ClusterManager Cmd CodeInfo Colon Complex Complex128 Complex32 Complex64 CompositeException Condition ConjArray ConjMatrix ConjVector Cptrdiff_t Cshort Csize_t Cssize_t Cstring Cuchar Cuint Cuintmax_t Culong Culonglong Cushort Cwchar_t Cwstring DataType Date DateFormat DateTime DenseArray DenseMatrix DenseVecOrMat DenseVector Diagonal Dict DimensionMismatch Dims DirectIndexString Display DivideError DomainError EOFError EachLine Enum Enumerate ErrorException Exception ExponentialBackOff Expr Factorization FileMonitor Float16 Float32 Float64 Function Future GlobalRef GotoNode HTML Hermitian IO IOBuffer IOContext IOStream IPAddr IPv4 IPv6 IndexCartesian IndexLinear IndexStyle InexactError InitError Int Int128 Int16 Int32 Int64 Int8 IntSet Integer InterruptException InvalidStateException Irrational KeyError LabelNode LinSpace LineNumberNode LoadError LowerTriangular MIME Matrix MersenneTwister Method MethodError MethodTable Module NTuple NewvarNode NullException Nullable Number ObjectIdDict OrdinalRange OutOfMemoryError OverflowError Pair ParseError PartialQuickSort PermutedDimsArray Pipe PollingFileWatcher ProcessExitedException Ptr QuoteNode RandomDevice Range RangeIndex Rational RawFD ReadOnlyMemoryError Real ReentrantLock Ref Regex RegexMatch RemoteChannel RemoteException RevString RoundingMode RowVector SSAValue SegmentationFault SerializationState Set SharedArray SharedMatrix SharedVector Signed SimpleVector Slot SlotNumber SparseMatrixCSC SparseVector StackFrame StackOverflowError StackTrace StepRange StepRangeLen StridedArray StridedMatrix StridedVecOrMat StridedVector String SubArray SubString SymTridiagonal Symbol Symmetric SystemError TCPSocket Task Text TextDisplay Timer Tridiagonal Tuple Type TypeError TypeMapEntry TypeMapLevel TypeName TypeVar TypedSlot UDPSocket UInt UInt128 UInt16 UInt32 UInt64 UInt8 UndefRefError UndefVarError UnicodeError UniformScaling Union UnionAll UnitRange Unsigned UpperTriangular Val Vararg VecElement VecOrMat Vector VersionNumber Void WeakKeyDict WeakRef WorkerConfig WorkerPool "},t="[A-Za-z_\\u00A1-\\uFFFF][A-Za-z_0-9\\u00A1-\\uFFFF]*",a={l:t,k:r,i:/<\//},n={cN:"number",b:/(\b0x[\d_]*(\.[\d_]*)?|0x\.\d[\d_]*)p[-+]?\d+|\b0[box][a-fA-F0-9][a-fA-F0-9_]*|(\b\d[\d_]*(\.[\d_]*)?|\.\d[\d_]*)([eEfF][-+]?\d+)?/,r:0},o={cN:"string",b:/'(.|\\[xXuU][a-zA-Z0-9]+)'/},i={cN:"subst",b:/\$\(/,e:/\)/,k:r},l={cN:"variable",b:"\\$"+t},c={cN:"string",c:[e.BE,i,l],v:[{b:/\w*"""/,e:/"""\w*/,r:10},{b:/\w*"/,e:/"\w*/}]},s={cN:"string",c:[e.BE,i,l],b:"`",e:"`"},d={cN:"meta",b:"@"+t},u={cN:"comment",v:[{b:"#=",e:"=#",r:10},{b:"#",e:"$"}]};return a.c=[n,o,c,s,d,u,e.HCM,{cN:"keyword",b:"\\b(((abstract|primitive)\\s+)type|(mutable\\s+)?struct)\\b"},{b:/<:/}],i.c=a.c,a});hljs.registerLanguage("coffeescript",function(e){var c={keyword:"in if for while finally new do return else break catch instanceof throw try this switch continue typeof delete debugger super yield import export from as default await then unless until loop of by when and or is isnt not",literal:"true false null undefined yes no on off",built_in:"npm require console print module global window document"},n="[A-Za-z$_][0-9A-Za-z$_]*",r={cN:"subst",b:/#\{/,e:/}/,k:c},i=[e.BNM,e.inherit(e.CNM,{starts:{e:"(\\s*/)?",r:0}}),{cN:"string",v:[{b:/'''/,e:/'''/,c:[e.BE]},{b:/'/,e:/'/,c:[e.BE]},{b:/"""/,e:/"""/,c:[e.BE,r]},{b:/"/,e:/"/,c:[e.BE,r]}]},{cN:"regexp",v:[{b:"///",e:"///",c:[r,e.HCM]},{b:"//[gim]*",r:0},{b:/\/(?![ *])(\\\/|.)*?\/[gim]*(?=\W|$)/}]},{b:"@"+n},{sL:"javascript",eB:!0,eE:!0,v:[{b:"```",e:"```"},{b:"`",e:"`"}]}];r.c=i;var s=e.inherit(e.TM,{b:n}),t="(\\(.*\\))?\\s*\\B[-=]>",o={cN:"params",b:"\\([^\\(]",rB:!0,c:[{b:/\(/,e:/\)/,k:c,c:["self"].concat(i)}]};return{aliases:["coffee","cson","iced"],k:c,i:/\/\*/,c:i.concat([e.C("###","###"),e.HCM,{cN:"function",b:"^\\s*"+n+"\\s*=\\s*"+t,e:"[-=]>",rB:!0,c:[s,o]},{b:/[:\(,=]\s*/,r:0,c:[{cN:"function",b:t,e:"[-=]>",rB:!0,c:[o]}]},{cN:"class",bK:"class",e:"$",i:/[:="\[\]]/,c:[{bK:"extends",eW:!0,i:/[:="\[\]]/,c:[s]},s]},{b:n+":",e:":",rB:!0,rE:!0,r:0}])}});hljs.registerLanguage("cpp",function(t){var e={cN:"keyword",b:"\\b[a-z\\d_]*_t\\b"},r={cN:"string",v:[{b:'(u8?|U)?L?"',e:'"',i:"\\n",c:[t.BE]},{b:'(u8?|U)?R"',e:'"',c:[t.BE]},{b:"'\\\\?.",e:"'",i:"."}]},s={cN:"number",v:[{b:"\\b(0b[01']+)"},{b:"(-?)\\b([\\d']+(\\.[\\d']*)?|\\.[\\d']+)(u|U|l|L|ul|UL|f|F|b|B)"},{b:"(-?)(\\b0[xX][a-fA-F0-9']+|(\\b[\\d']+(\\.[\\d']*)?|\\.[\\d']+)([eE][-+]?[\\d']+)?)"}],r:0},i={cN:"meta",b:/#\s*[a-z]+\b/,e:/$/,k:{"meta-keyword":"if else elif endif define undef warning error line pragma ifdef ifndef include"},c:[{b:/\\\n/,r:0},t.inherit(r,{cN:"meta-string"}),{cN:"meta-string",b:/<[^\n>]*>/,e:/$/,i:"\\n"},t.CLCM,t.CBCM]},a=t.IR+"\\s*\\(",c={keyword:"int float while private char catch import module export virtual operator sizeof dynamic_cast|10 typedef const_cast|10 const for static_cast|10 union namespace unsigned long volatile static protected bool template mutable if public friend do goto auto void enum else break extern using asm case typeid short reinterpret_cast|10 default double register explicit signed typename try this switch continue inline delete alignof constexpr decltype noexcept static_assert thread_local restrict _Bool complex _Complex _Imaginary atomic_bool atomic_char atomic_schar atomic_uchar atomic_short atomic_ushort atomic_int atomic_uint atomic_long atomic_ulong atomic_llong atomic_ullong new throw return and or not",built_in:"std string cin cout cerr clog stdin stdout stderr stringstream istringstream ostringstream auto_ptr deque list queue stack vector map set bitset multiset multimap unordered_set unordered_map unordered_multiset unordered_multimap array shared_ptr abort abs acos asin atan2 atan calloc ceil cosh cos exit exp fabs floor fmod fprintf fputs free frexp fscanf isalnum isalpha iscntrl isdigit isgraph islower isprint ispunct isspace isupper isxdigit tolower toupper labs ldexp log10 log malloc realloc memchr memcmp memcpy memset modf pow printf putchar puts scanf sinh sin snprintf sprintf sqrt sscanf strcat strchr strcmp strcpy strcspn strlen strncat strncmp strncpy strpbrk strrchr strspn strstr tanh tan vfprintf vprintf vsprintf endl initializer_list unique_ptr",literal:"true false nullptr NULL"},n=[e,t.CLCM,t.CBCM,s,r];return{aliases:["c","cc","h","c++","h++","hpp"],k:c,i:"</",c:n.concat([i,{b:"\\b(deque|list|queue|stack|vector|map|set|bitset|multiset|multimap|unordered_map|unordered_set|unordered_multiset|unordered_multimap|array)\\s*<",e:">",k:c,c:["self",e]},{b:t.IR+"::",k:c},{v:[{b:/=/,e:/;/},{b:/\(/,e:/\)/},{bK:"new throw return else",e:/;/}],k:c,c:n.concat([{b:/\(/,e:/\)/,k:c,c:n.concat(["self"]),r:0}]),r:0},{cN:"function",b:"("+t.IR+"[\\*&\\s]+)+"+a,rB:!0,e:/[{;=]/,eE:!0,k:c,i:/[^\w\s\*&]/,c:[{b:a,rB:!0,c:[t.TM],r:0},{cN:"params",b:/\(/,e:/\)/,k:c,r:0,c:[t.CLCM,t.CBCM,r,s,e]},t.CLCM,t.CBCM,i]},{cN:"class",bK:"class struct",e:/[{;:]/,c:[{b:/</,e:/>/,c:["self"]},t.TM]}]),exports:{preprocessor:i,strings:r,k:c}}});hljs.registerLanguage("ruby",function(e){var b="[a-zA-Z_]\\w*[!?=]?|[-+~]\\@|<<|>>|=~|===?|<=>|[<>]=?|\\*\\*|[-/+%^&*~`|]|\\[\\]=?",r={keyword:"and then defined module in return redo if BEGIN retry end for self when next until do begin unless END rescue else break undef not super class case require yield alias while ensure elsif or include attr_reader attr_writer attr_accessor",literal:"true false nil"},c={cN:"doctag",b:"@[A-Za-z]+"},a={b:"#<",e:">"},s=[e.C("#","$",{c:[c]}),e.C("^\\=begin","^\\=end",{c:[c],r:10}),e.C("^__END__","\\n$")],n={cN:"subst",b:"#\\{",e:"}",k:r},t={cN:"string",c:[e.BE,n],v:[{b:/'/,e:/'/},{b:/"/,e:/"/},{b:/`/,e:/`/},{b:"%[qQwWx]?\\(",e:"\\)"},{b:"%[qQwWx]?\\[",e:"\\]"},{b:"%[qQwWx]?{",e:"}"},{b:"%[qQwWx]?<",e:">"},{b:"%[qQwWx]?/",e:"/"},{b:"%[qQwWx]?%",e:"%"},{b:"%[qQwWx]?-",e:"-"},{b:"%[qQwWx]?\\|",e:"\\|"},{b:/\B\?(\\\d{1,3}|\\x[A-Fa-f0-9]{1,2}|\\u[A-Fa-f0-9]{4}|\\?\S)\b/},{b:/<<(-?)\w+$/,e:/^\s*\w+$/}]},i={cN:"params",b:"\\(",e:"\\)",endsParent:!0,k:r},d=[t,a,{cN:"class",bK:"class module",e:"$|;",i:/=/,c:[e.inherit(e.TM,{b:"[A-Za-z_]\\w*(::\\w+)*(\\?|\\!)?"}),{b:"<\\s*",c:[{b:"("+e.IR+"::)?"+e.IR}]}].concat(s)},{cN:"function",bK:"def",e:"$|;",c:[e.inherit(e.TM,{b:b}),i].concat(s)},{b:e.IR+"::"},{cN:"symbol",b:e.UIR+"(\\!|\\?)?:",r:0},{cN:"symbol",b:":(?!\\s)",c:[t,{b:b}],r:0},{cN:"number",b:"(\\b0[0-7_]+)|(\\b0x[0-9a-fA-F_]+)|(\\b[1-9][0-9_]*(\\.[0-9_]+)?)|[0_]\\b",r:0},{b:"(\\$\\W)|((\\$|\\@\\@?)(\\w+))"},{cN:"params",b:/\|/,e:/\|/,k:r},{b:"("+e.RSR+"|unless)\\s*",k:"unless",c:[a,{cN:"regexp",c:[e.BE,n],i:/\n/,v:[{b:"/",e:"/[a-z]*"},{b:"%r{",e:"}[a-z]*"},{b:"%r\\(",e:"\\)[a-z]*"},{b:"%r!",e:"![a-z]*"},{b:"%r\\[",e:"\\][a-z]*"}]}].concat(s),r:0}].concat(s);n.c=d,i.c=d;var l="[>?]>",o="[\\w#]+\\(\\w+\\):\\d+:\\d+>",u="(\\w+-)?\\d+\\.\\d+\\.\\d(p\\d+)?[^>]+>",w=[{b:/^\s*=>/,starts:{e:"$",c:d}},{cN:"meta",b:"^("+l+"|"+o+"|"+u+")",starts:{e:"$",c:d}}];return{aliases:["rb","gemspec","podspec","thor","irb"],k:r,i:/\/\*/,c:s.concat(w).concat(d)}});hljs.registerLanguage("yaml",function(e){var b="true false yes no null",a="^[ \\-]*",r="[a-zA-Z_][\\w\\-]*",t={cN:"attr",v:[{b:a+r+":"},{b:a+'"'+r+'":'},{b:a+"'"+r+"':"}]},c={cN:"template-variable",v:[{b:"{{",e:"}}"},{b:"%{",e:"}"}]},l={cN:"string",r:0,v:[{b:/'/,e:/'/},{b:/"/,e:/"/},{b:/\S+/}],c:[e.BE,c]};return{cI:!0,aliases:["yml","YAML","yaml"],c:[t,{cN:"meta",b:"^---s*$",r:10},{cN:"string",b:"[\\|>] *$",rE:!0,c:l.c,e:t.v[0].b},{b:"<%[%=-]?",e:"[%-]?%>",sL:"ruby",eB:!0,eE:!0,r:0},{cN:"type",b:"!!"+e.UIR},{cN:"meta",b:"&"+e.UIR+"$"},{cN:"meta",b:"\\*"+e.UIR+"$"},{cN:"bullet",b:"^ *-",r:0},e.HCM,{bK:b,k:{literal:b}},e.CNM,l]}});hljs.registerLanguage("css",function(e){var c="[a-zA-Z-][a-zA-Z0-9_-]*",t={b:/[A-Z\_\.\-]+\s*:/,rB:!0,e:";",eW:!0,c:[{cN:"attribute",b:/\S/,e:":",eE:!0,starts:{eW:!0,eE:!0,c:[{b:/[\w-]+\(/,rB:!0,c:[{cN:"built_in",b:/[\w-]+/},{b:/\(/,e:/\)/,c:[e.ASM,e.QSM]}]},e.CSSNM,e.QSM,e.ASM,e.CBCM,{cN:"number",b:"#[0-9A-Fa-f]+"},{cN:"meta",b:"!important"}]}}]};return{cI:!0,i:/[=\/|'\$]/,c:[e.CBCM,{cN:"selector-id",b:/#[A-Za-z0-9_-]+/},{cN:"selector-class",b:/\.[A-Za-z0-9_-]+/},{cN:"selector-attr",b:/\[/,e:/\]/,i:"$"},{cN:"selector-pseudo",b:/:(:)?[a-zA-Z0-9\_\-\+\(\)"'.]+/},{b:"@(font-face|page)",l:"[a-z-]+",k:"font-face page"},{b:"@",e:"[{;]",i:/:/,c:[{cN:"keyword",b:/\w+/},{b:/\s/,eW:!0,eE:!0,r:0,c:[e.ASM,e.QSM,e.CSSNM]}]},{cN:"selector-tag",b:c,r:0},{b:"{",e:"}",i:/\S/,c:[e.CBCM,t]}]}});hljs.registerLanguage("fortran",function(e){var t={cN:"params",b:"\\(",e:"\\)"},n={literal:".False. .True.",keyword:"kind do while private call intrinsic where elsewhere type endtype endmodule endselect endinterface end enddo endif if forall endforall only contains default return stop then public subroutine|10 function program .and. .or. .not. .le. .eq. .ge. .gt. .lt. goto save else use module select case access blank direct exist file fmt form formatted iostat name named nextrec number opened rec recl sequential status unformatted unit continue format pause cycle exit c_null_char c_alert c_backspace c_form_feed flush wait decimal round iomsg synchronous nopass non_overridable pass protected volatile abstract extends import non_intrinsic value deferred generic final enumerator class associate bind enum c_int c_short c_long c_long_long c_signed_char c_size_t c_int8_t c_int16_t c_int32_t c_int64_t c_int_least8_t c_int_least16_t c_int_least32_t c_int_least64_t c_int_fast8_t c_int_fast16_t c_int_fast32_t c_int_fast64_t c_intmax_t C_intptr_t c_float c_double c_long_double c_float_complex c_double_complex c_long_double_complex c_bool c_char c_null_ptr c_null_funptr c_new_line c_carriage_return c_horizontal_tab c_vertical_tab iso_c_binding c_loc c_funloc c_associated  c_f_pointer c_ptr c_funptr iso_fortran_env character_storage_size error_unit file_storage_size input_unit iostat_end iostat_eor numeric_storage_size output_unit c_f_procpointer ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode newunit contiguous recursive pad position action delim readwrite eor advance nml interface procedure namelist include sequence elemental pure integer real character complex logical dimension allocatable|10 parameter external implicit|10 none double precision assign intent optional pointer target in out common equivalence data",built_in:"alog alog10 amax0 amax1 amin0 amin1 amod cabs ccos cexp clog csin csqrt dabs dacos dasin datan datan2 dcos dcosh ddim dexp dint dlog dlog10 dmax1 dmin1 dmod dnint dsign dsin dsinh dsqrt dtan dtanh float iabs idim idint idnint ifix isign max0 max1 min0 min1 sngl algama cdabs cdcos cdexp cdlog cdsin cdsqrt cqabs cqcos cqexp cqlog cqsin cqsqrt dcmplx dconjg derf derfc dfloat dgamma dimag dlgama iqint qabs qacos qasin qatan qatan2 qcmplx qconjg qcos qcosh qdim qerf qerfc qexp qgamma qimag qlgama qlog qlog10 qmax1 qmin1 qmod qnint qsign qsin qsinh qsqrt qtan qtanh abs acos aimag aint anint asin atan atan2 char cmplx conjg cos cosh exp ichar index int log log10 max min nint sign sin sinh sqrt tan tanh print write dim lge lgt lle llt mod nullify allocate deallocate adjustl adjustr all allocated any associated bit_size btest ceiling count cshift date_and_time digits dot_product eoshift epsilon exponent floor fraction huge iand ibclr ibits ibset ieor ior ishft ishftc lbound len_trim matmul maxexponent maxloc maxval merge minexponent minloc minval modulo mvbits nearest pack present product radix random_number random_seed range repeat reshape rrspacing scale scan selected_int_kind selected_real_kind set_exponent shape size spacing spread sum system_clock tiny transpose trim ubound unpack verify achar iachar transfer dble entry dprod cpu_time command_argument_count get_command get_command_argument get_environment_variable is_iostat_end ieee_arithmetic ieee_support_underflow_control ieee_get_underflow_mode ieee_set_underflow_mode is_iostat_eor move_alloc new_line selected_char_kind same_type_as extends_type_ofacosh asinh atanh bessel_j0 bessel_j1 bessel_jn bessel_y0 bessel_y1 bessel_yn erf erfc erfc_scaled gamma log_gamma hypot norm2 atomic_define atomic_ref execute_command_line leadz trailz storage_size merge_bits bge bgt ble blt dshiftl dshiftr findloc iall iany iparity image_index lcobound ucobound maskl maskr num_images parity popcnt poppar shifta shiftl shiftr this_image"};return{cI:!0,aliases:["f90","f95"],k:n,i:/\/\*/,c:[e.inherit(e.ASM,{cN:"string",r:0}),e.inherit(e.QSM,{cN:"string",r:0}),{cN:"function",bK:"subroutine function program",i:"[${=\\n]",c:[e.UTM,t]},e.C("!","$",{r:0}),{cN:"number",b:"(?=\\b|\\+|\\-|\\.)(?=\\.\\d|\\d)(?:\\d+)?(?:\\.?\\d*)(?:[de][+-]?\\d+)?\\b\\.?",r:0}]}});hljs.registerLanguage("awk",function(e){var r={cN:"variable",v:[{b:/\$[\w\d#@][\w\d_]*/},{b:/\$\{(.*?)}/}]},b="BEGIN END if else while do for in break continue delete next nextfile function func exit|10",n={cN:"string",c:[e.BE],v:[{b:/(u|b)?r?'''/,e:/'''/,r:10},{b:/(u|b)?r?"""/,e:/"""/,r:10},{b:/(u|r|ur)'/,e:/'/,r:10},{b:/(u|r|ur)"/,e:/"/,r:10},{b:/(b|br)'/,e:/'/},{b:/(b|br)"/,e:/"/},e.ASM,e.QSM]};return{k:{keyword:b},c:[r,n,e.RM,e.HCM,e.NM]}});hljs.registerLanguage("makefile",function(e){var i={cN:"variable",v:[{b:"\\$\\("+e.UIR+"\\)",c:[e.BE]},{b:/\$[@%<?\^\+\*]/}]},r={cN:"string",b:/"/,e:/"/,c:[e.BE,i]},a={cN:"variable",b:/\$\([\w-]+\s/,e:/\)/,k:{built_in:"subst patsubst strip findstring filter filter-out sort word wordlist firstword lastword dir notdir suffix basename addsuffix addprefix join wildcard realpath abspath error warning shell origin flavor foreach if or and call eval file value"},c:[i]},n={b:"^"+e.UIR+"\\s*[:+?]?=",i:"\\n",rB:!0,c:[{b:"^"+e.UIR,e:"[:+?]?=",eE:!0}]},t={cN:"meta",b:/^\.PHONY:/,e:/$/,k:{"meta-keyword":".PHONY"},l:/[\.\w]+/},l={cN:"section",b:/^[^\s]+:/,e:/$/,c:[i]};return{aliases:["mk","mak"],k:"define endef undefine ifdef ifndef ifeq ifneq else endif include -include sinclude override export unexport private vpath",l:/[\w-]+/,c:[e.HCM,i,r,a,n,t,l]}});hljs.registerLanguage("java",function(e){var a="[À-ʸa-zA-Z_$][À-ʸa-zA-Z_$0-9]*",t=a+"(<"+a+"(\\s*,\\s*"+a+")*>)?",r="false synchronized int abstract float private char boolean static null if const for true while long strictfp finally protected import native final void enum else break transient catch instanceof byte super volatile case assert short package default double public try this switch continue throws protected public private module requires exports do",s="\\b(0[bB]([01]+[01_]+[01]+|[01]+)|0[xX]([a-fA-F0-9]+[a-fA-F0-9_]+[a-fA-F0-9]+|[a-fA-F0-9]+)|(([\\d]+[\\d_]+[\\d]+|[\\d]+)(\\.([\\d]+[\\d_]+[\\d]+|[\\d]+))?|\\.([\\d]+[\\d_]+[\\d]+|[\\d]+))([eE][-+]?\\d+)?)[lLfF]?",c={cN:"number",b:s,r:0};return{aliases:["jsp"],k:r,i:/<\/|#/,c:[e.C("/\\*\\*","\\*/",{r:0,c:[{b:/\w+@/,r:0},{cN:"doctag",b:"@[A-Za-z]+"}]}),e.CLCM,e.CBCM,e.ASM,e.QSM,{cN:"class",bK:"class interface",e:/[{;=]/,eE:!0,k:"class interface",i:/[:"\[\]]/,c:[{bK:"extends implements"},e.UTM]},{bK:"new throw return else",r:0},{cN:"function",b:"("+t+"\\s+)+"+e.UIR+"\\s*\\(",rB:!0,e:/[{;=]/,eE:!0,k:r,c:[{b:e.UIR+"\\s*\\(",rB:!0,r:0,c:[e.UTM]},{cN:"params",b:/\(/,e:/\)/,k:r,r:0,c:[e.ASM,e.QSM,e.CNM,e.CBCM]},e.CLCM,e.CBCM]},c,{cN:"meta",b:"@[A-Za-z]+"}]}});hljs.registerLanguage("stan",function(e){return{c:[e.HCM,e.CLCM,e.CBCM,{b:e.UIR,l:e.UIR,k:{name:"for in while repeat until if then else",symbol:"bernoulli bernoulli_logit binomial binomial_logit beta_binomial hypergeometric categorical categorical_logit ordered_logistic neg_binomial neg_binomial_2 neg_binomial_2_log poisson poisson_log multinomial normal exp_mod_normal skew_normal student_t cauchy double_exponential logistic gumbel lognormal chi_square inv_chi_square scaled_inv_chi_square exponential inv_gamma weibull frechet rayleigh wiener pareto pareto_type_2 von_mises uniform multi_normal multi_normal_prec multi_normal_cholesky multi_gp multi_gp_cholesky multi_student_t gaussian_dlm_obs dirichlet lkj_corr lkj_corr_cholesky wishart inv_wishart","selector-tag":"int real vector simplex unit_vector ordered positive_ordered row_vector matrix cholesky_factor_corr cholesky_factor_cov corr_matrix cov_matrix",title:"functions model data parameters quantities transformed generated",literal:"true false"},r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"0[xX][0-9a-fA-F]+[Li]?\\b",r:0},{cN:"number",b:"\\d+(?:[eE][+\\-]?\\d*)?L\\b",r:0},{cN:"number",b:"\\d+\\.(?!\\d)(?:i\\b)?",r:0},{cN:"number",b:"\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",r:0}]}});hljs.registerLanguage("javascript",function(e){var r="[A-Za-z$_][0-9A-Za-z$_]*",t={keyword:"in of if for while finally var new function do return void else break catch instanceof with throw case default try this switch continue typeof delete let yield const export super debugger as async await static import from as",literal:"true false null undefined NaN Infinity",built_in:"eval isFinite isNaN parseFloat parseInt decodeURI decodeURIComponent encodeURI encodeURIComponent escape unescape Object Function Boolean Error EvalError InternalError RangeError ReferenceError StopIteration SyntaxError TypeError URIError Number Math Date String RegExp Array Float32Array Float64Array Int16Array Int32Array Int8Array Uint16Array Uint32Array Uint8Array Uint8ClampedArray ArrayBuffer DataView JSON Intl arguments require module console window document Symbol Set Map WeakSet WeakMap Proxy Reflect Promise"},a={cN:"number",v:[{b:"\\b(0[bB][01]+)"},{b:"\\b(0[oO][0-7]+)"},{b:e.CNR}],r:0},n={cN:"subst",b:"\\$\\{",e:"\\}",k:t,c:[]},c={cN:"string",b:"`",e:"`",c:[e.BE,n]};n.c=[e.ASM,e.QSM,c,a,e.RM];var s=n.c.concat([e.CBCM,e.CLCM]);return{aliases:["js","jsx"],k:t,c:[{cN:"meta",r:10,b:/^\s*['"]use (strict|asm)['"]/},{cN:"meta",b:/^#!/,e:/$/},e.ASM,e.QSM,c,e.CLCM,e.CBCM,a,{b:/[{,]\s*/,r:0,c:[{b:r+"\\s*:",rB:!0,r:0,c:[{cN:"attr",b:r,r:0}]}]},{b:"("+e.RSR+"|\\b(case|return|throw)\\b)\\s*",k:"return throw case",c:[e.CLCM,e.CBCM,e.RM,{cN:"function",b:"(\\(.*?\\)|"+r+")\\s*=>",rB:!0,e:"\\s*=>",c:[{cN:"params",v:[{b:r},{b:/\(\s*\)/},{b:/\(/,e:/\)/,eB:!0,eE:!0,k:t,c:s}]}]},{b:/</,e:/(\/\w+|\w+\/)>/,sL:"xml",c:[{b:/<\w+\s*\/>/,skip:!0},{b:/<\w+/,e:/(\/\w+|\w+\/)>/,skip:!0,c:[{b:/<\w+\s*\/>/,skip:!0},"self"]}]}],r:0},{cN:"function",bK:"function",e:/\{/,eE:!0,c:[e.inherit(e.TM,{b:r}),{cN:"params",b:/\(/,e:/\)/,eB:!0,eE:!0,c:s}],i:/\[|%/},{b:/\$[(.]/},e.METHOD_GUARD,{cN:"class",bK:"class",e:/[{;=]/,eE:!0,i:/[:"\[\]]/,c:[{bK:"extends"},e.UTM]},{bK:"constructor",e:/\{/,eE:!0}],i:/#(?!!)/}});hljs.registerLanguage("tex",function(c){var e={cN:"tag",b:/\\/,r:0,c:[{cN:"name",v:[{b:/[a-zA-Zа-яА-я]+[*]?/},{b:/[^a-zA-Zа-яА-я0-9]/}],starts:{eW:!0,r:0,c:[{cN:"string",v:[{b:/\[/,e:/\]/},{b:/\{/,e:/\}/}]},{b:/\s*=\s*/,eW:!0,r:0,c:[{cN:"number",b:/-?\d*\.?\d+(pt|pc|mm|cm|in|dd|cc|ex|em)?/}]}]}}]};return{c:[e,{cN:"formula",c:[e],r:0,v:[{b:/\$\$/,e:/\$\$/},{b:/\$/,e:/\$/}]},c.C("%","$",{r:0})]}});hljs.registerLanguage("xml",function(s){var e="[A-Za-z0-9\\._:-]+",t={eW:!0,i:/</,r:0,c:[{cN:"attr",b:e,r:0},{b:/=\s*/,r:0,c:[{cN:"string",endsParent:!0,v:[{b:/"/,e:/"/},{b:/'/,e:/'/},{b:/[^\s"'=<>`]+/}]}]}]};return{aliases:["html","xhtml","rss","atom","xjb","xsd","xsl","plist"],cI:!0,c:[{cN:"meta",b:"<!DOCTYPE",e:">",r:10,c:[{b:"\\[",e:"\\]"}]},s.C("<!--","-->",{r:10}),{b:"<\\!\\[CDATA\\[",e:"\\]\\]>",r:10},{b:/<\?(php)?/,e:/\?>/,sL:"php",c:[{b:"/\\*",e:"\\*/",skip:!0}]},{cN:"tag",b:"<style(?=\\s|>|$)",e:">",k:{name:"style"},c:[t],starts:{e:"</style>",rE:!0,sL:["css","xml"]}},{cN:"tag",b:"<script(?=\\s|>|$)",e:">",k:{name:"script"},c:[t],starts:{e:"</script>",rE:!0,sL:["actionscript","javascript","handlebars","xml"]}},{cN:"meta",v:[{b:/<\?xml/,e:/\?>/,r:10},{b:/<\?\w+/,e:/\?>/}]},{cN:"tag",b:"</?",e:"/?>",c:[{cN:"name",b:/[^\/><\s]+/,r:0},t]}]}});hljs.registerLanguage("markdown",function(e){return{aliases:["md","mkdown","mkd"],c:[{cN:"section",v:[{b:"^#{1,6}",e:"$"},{b:"^.+?\\n[=-]{2,}$"}]},{b:"<",e:">",sL:"xml",r:0},{cN:"bullet",b:"^([*+-]|(\\d+\\.))\\s+"},{cN:"strong",b:"[*_]{2}.+?[*_]{2}"},{cN:"emphasis",v:[{b:"\\*.+?\\*"},{b:"_.+?_",r:0}]},{cN:"quote",b:"^>\\s+",e:"$"},{cN:"code",v:[{b:"^```w*s*$",e:"^```s*$"},{b:"`.+?`"},{b:"^( {4}|	)",e:"$",r:0}]},{b:"^[-\\*]{3,}",e:"$"},{b:"\\[.+?\\][\\(\\[].*?[\\)\\]]",rB:!0,c:[{cN:"string",b:"\\[",e:"\\]",eB:!0,rE:!0,r:0},{cN:"link",b:"\\]\\(",e:"\\)",eB:!0,eE:!0},{cN:"symbol",b:"\\]\\[",e:"\\]",eB:!0,eE:!0}],r:10},{b:/^\[[^\n]+\]:/,rB:!0,c:[{cN:"symbol",b:/\[/,e:/\]/,eB:!0,eE:!0},{cN:"link",b:/:\s*/,e:/$/,eB:!0}]}]}});hljs.registerLanguage("json",function(e){var i={literal:"true false null"},n=[e.QSM,e.CNM],r={e:",",eW:!0,eE:!0,c:n,k:i},t={b:"{",e:"}",c:[{cN:"attr",b:/"/,e:/"/,c:[e.BE],i:"\\n"},e.inherit(r,{b:/:/})],i:"\\S"},c={b:"\\[",e:"\\]",c:[e.inherit(r)],i:"\\S"};return n.splice(n.length,0,t,c),{c:n,k:i,i:"\\S"}});"></script>

<style type="text/css">
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
span.underline{text-decoration: underline;}
div.column{display: inline-block; vertical-align: top; width: 50%;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
</style>

<style type="text/css">code{white-space: pre;}</style>
<script type="text/javascript">
if (window.hljs) {
  hljs.configure({languages: []});
  hljs.initHighlightingOnLoad();
  if (document.readyState && document.readyState === "complete") {
    window.setTimeout(function() { hljs.initHighlighting(); }, 0);
  }
}
</script>









<style type="text/css">
.main-container {
max-width: 940px;
margin-left: auto;
margin-right: auto;
}
img {
max-width:100%;
}
.tabbed-pane {
padding-top: 12px;
}
.html-widget {
margin-bottom: 20px;
}
button.code-folding-btn:focus {
outline: none;
}
summary {
display: list-item;
}
details > summary > p:only-child {
display: inline;
}
pre code {
padding: 0;
}
</style>



<!-- tabsets -->

<style type="text/css">
.tabset-dropdown > .nav-tabs {
display: inline-table;
max-height: 500px;
min-height: 44px;
overflow-y: auto;
border: 1px solid #ddd;
border-radius: 4px;
}
.tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before {
content: "\e259";
font-family: 'Glyphicons Halflings';
display: inline-block;
padding: 10px;
border-right: 1px solid #ddd;
}
.tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before {
content: "\e258";
font-family: 'Glyphicons Halflings';
border: none;
}
.tabset-dropdown > .nav-tabs > li.active {
display: block;
}
.tabset-dropdown > .nav-tabs > li > a,
.tabset-dropdown > .nav-tabs > li > a:focus,
.tabset-dropdown > .nav-tabs > li > a:hover {
border: none;
display: inline-block;
border-radius: 4px;
background-color: transparent;
}
.tabset-dropdown > .nav-tabs.nav-tabs-open > li {
display: block;
float: none;
}
.tabset-dropdown > .nav-tabs > li {
display: none;
}
</style>

<!-- code folding -->




</head>

<body>


<div class="container-fluid main-container">




<div id="header">



<h1 class="title toc-ignore">regression_analysis.R</h1>
<h4 class="author">sebastian</h4>
<h4 class="date">2025-10-15</h4>

</div>


<pre class="r"><code>#### Paper title: Introducing the MAVERICK dataset
#### Purpose: Regression analysis and robustness tests
#### Author: Sebastian van Baalen &amp; Kristine Höglund
#### Last updated: 2025-10-14

#### Front matter ####

# Install packages

# Uncomment to install packages

# install.packages(&quot;here&quot;) # To use relative paths
# install.packages(&quot;devtools&quot;) # To install packages from Github
# install.packages(&quot;MASS&quot;) &quot; To use negative binomial regression
# install.packages(&quot;eventreport&quot;) # To load and aggregate the MAVERICK data
# install.packages(&quot;tidyverse&quot;) # To work with tidy data
# install.packages(&quot;sf&quot;) # To work with spatial data
# install.packages(&quot;lmtest&quot;) # To calculate robust clustered standard errors
# install.packages(&quot;sandwich&quot;) # To calculate robust clustered standard errors
# install.packages(&quot;stargazer&quot;) # To create regression tables
# install.packages(&quot;marginaleffects&quot;) # To calculate marginal effects
# install.packages(&quot;ggplot2&quot;) # To visualize data
# install.packages(&quot;wesanderson&quot;) # To use the Wes Anderson color palettes
# install.packages(&quot;car&quot;) # To conduct hypothesis testing
# install.packages(&quot;scales&quot;) # To adapt plot scales

# Install the vdemdata package from Github

# devtools::install_github(&quot;vdeminstitute/vdemdata&quot;) # To load the V-Dem data

# Install the eventreport package from Github

# devtools::install_github(&quot;sebastianvanbaalen/eventreport&quot;) # To load and aggregate the MAVERICK data

# Load packages

suppressWarnings(suppressMessages(suppressPackageStartupMessages({
  library(here) # To use relative paths
  library(MASS) # To use negative binomial regression
  library(eventreport) # To load and aggregate the MAVERICK data
  library(tidyverse) # To work with tidy data
  library(sf) # To work with spatial data
  library(lmtest) # To calculate robust clustered standard errors
  library(sandwich) # To calculate robust clustered standard errors
  library(stargazer) # To create regression tables
  library(marginaleffects) # To calculate marginal effects
  library(scales) # To adapt plot scales
  library(fixest) # For calculating Conley standard errors 
  library(ggplot2) # To visualize data
  library(wesanderson) # To use the Wes Anderson color palettes
  library(car) # To conduct hypothesis testing
})))

# Load MAVERICK data using the eventreport package

maverick_inf &lt;- aggregate_maverick_inf()

maverick_rep &lt;- aggregate_maverick_rep()

maverick_con &lt;- aggregate_maverick_con()

maverick &lt;- maverick_event_report

# Load shapefiles

# Available at: https://data.humdata.org/dataset/geoboundaries-admin-boundaries-for-cote-d-ivoire

shapefile_civ &lt;- st_read(&#39;civ_admbnda_adm1_cntig_ocha_itos_20180706/civ_admbnda_adm1_cntig_ocha_itos_20180706.shp&#39;)</code></pre>
<pre><code>## Reading layer `civ_admbnda_adm1_cntig_ocha_itos_20180706&#39; from data source 
##   `/Users/sebastian/Documents/Datasets and scripts/Replication files/JPR 2025/civ_admbnda_adm1_cntig_ocha_itos_20180706/civ_admbnda_adm1_cntig_ocha_itos_20180706.shp&#39; 
##   using driver `ESRI Shapefile&#39;
## Simple feature collection with 33 features and 12 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -8.584147 ymin: 4.360553 xmax: -2.492309 ymax: 10.74565
## Geodetic CRS:  WGS 84</code></pre>
<pre class="r"><code># Available at: https://data.humdata.org/dataset/geoboundaries-admin-boundaries-for-kenya

shapefile_ke &lt;- st_read(&#39;geoBoundaries-KEN-ADM1-all/geoBoundaries-KEN-ADM1.shp&#39;)</code></pre>
<pre><code>## Reading layer `geoBoundaries-KEN-ADM1&#39; from data source 
##   `/Users/sebastian/Documents/Datasets and scripts/Replication files/JPR 2025/geoBoundaries-KEN-ADM1-all/geoBoundaries-KEN-ADM1.shp&#39; 
##   using driver `ESRI Shapefile&#39;
## Simple feature collection with 47 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 33.91182 ymin: -4.702271 xmax: 41.90626 ymax: 5.430648
## Geodetic CRS:  WGS 84</code></pre>
<pre class="r"><code># Define custom plot theme

my_theme &lt;- theme_bw() +
  theme(
    plot.margin = margin(1, 1, 1, 1, &quot;cm&quot;),
    axis.title.y = element_text(margin = margin(t = 0, r = 10, b = 0, l = 0), size = 20),
    axis.title.y.right = element_text(margin = margin(t = 0, r = 0, b = 0, l = 10), size = 20),
    axis.title.x = element_text(margin = margin(t = 10, r = 0, b = 0, l = 0), size = 20),
    axis.text.x = element_text(size = 18),
    axis.text.y = element_text(size = 18),
    legend.position = &quot;bottom&quot;,
    legend.title = element_text(size = 18),
    legend.text = element_text(size = 18),
    strip.text = element_text(size = 18),
    strip.background = element_rect(fill = &quot;white&quot;, color = &quot;black&quot;),
    plot.title = element_text(size = 20, face = &quot;bold&quot;, hjust = 0, margin = margin (t = 0, r = 0, b = 10, l = 0)),
    plot.subtitle = element_text(size = 14, hjust = 0, margin = margin (t = 0, r = 0, b = 40, l = 0)),
    plot.caption = element_text(size = 16, hjust = 1, margin = margin (t = 20, r = 0, b = 0, l = 0)),
    panel.grid.minor = element_blank(),
    panel.grid.major = element_blank()
  )

#### Prepare main dataset ####

# Make dataset spatial

maverick_sf &lt;- maverick_rep %&gt;%
  filter(geo_precision &gt; 2 &amp; !is.na(latitude) &amp; !is.na(longitude) &amp; latitude != 0 &amp; longitude != 0) %&gt;% 
  mutate(year = year(as.Date(date_start, format = &quot;%Y-%m-%d&quot;))) %&gt;% 
  mutate(country_year = paste(country, year, sep = &quot;_&quot;)) %&gt;% 
  st_as_sf(coords = c(&quot;longitude&quot;, &quot;latitude&quot;), crs = 4326, remove = FALSE)

# Add district names from shapefiles

maverick_sf &lt;- maverick_sf %&gt;%
  st_join(shapefile_civ %&gt;% select(ADM1_FR), left = TRUE) %&gt;% 
  st_join(shapefile_ke %&gt;% select(shapeName), left = TRUE) %&gt;% 
  mutate(
    district = case_when(
      country == &quot;Ivory Coast&quot; ~ ADM1_FR,
      TRUE ~ shapeName
    )
  ) %&gt;%
  filter(!is.na(district)) %&gt;% 
  select(-ADM1_FR, -shapeName)

# Load PRIO-Grid

prio_grid &lt;- read.csv(&quot;PRIO-GRID Static Variables - 2024-06-14.csv&quot;)

# Load PRIO-Grid shapefile

shapefile_prio_grid &lt;- st_read(&quot;priogrid_cellshp/priogrid_cell.shp&quot;)</code></pre>
<pre><code>## Reading layer `priogrid_cell&#39; from data source 
##   `/Users/sebastian/Documents/Datasets and scripts/Replication files/JPR 2025/priogrid_cellshp/priogrid_cell.shp&#39; 
##   using driver `ESRI Shapefile&#39;
## Simple feature collection with 259200 features and 5 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -180 ymin: -90 xmax: 180 ymax: 90
## Geodetic CRS:  WGS 84</code></pre>
<pre class="r"><code># Join PRIO-Grid shapefile with PRIO-Grid dataset

shapefile_prio_grid &lt;- shapefile_prio_grid %&gt;% 
  left_join(prio_grid, by = &quot;gid&quot;)

# Join dataset with PRIO-Grid dataset

maverick_sf &lt;- maverick_sf %&gt;% 
  st_join(shapefile_prio_grid)

# Create variables for analysis 

maverick_sf &lt;- maverick_sf %&gt;% 
  # Create security force involvement variable
  mutate(
    event_context = case_when(
      event_context == &quot;Canvasing&quot; ~ &quot;Other context&quot;,
      event_context == &quot;Incumbent campaign event&quot; ~ &quot;Campaign event&quot;,
      event_context == &quot;Arrest or raid&quot; ~ &quot;Military operation&quot;,
      event_context == &quot;Opposition campaign event&quot; ~ &quot;Campaign event&quot;,
      event_context == &quot;Riot&quot; ~ &quot;Protest or riot&quot;,
      event_context == &quot;Protest&quot; ~ &quot;Protest or riot&quot;,
      event_context == &quot;Security force crack-down on protest&quot; ~ &quot;Protest or riot&quot;,
      event_context == &quot;Vote counting&quot; ~ &quot;Voting procedures&quot;,
      event_context == &quot;Voting&quot; ~ &quot;Voting procedures&quot;,
      event_context == &quot;Indeterminate&quot; ~ &quot;Unknown&quot;,
      TRUE ~ event_context
    ),
    sf_involve = case_when(
      actor1_type == &quot;Security forces&quot; &amp; actor1_bystander != 1 ~ 1,
      actor2_type == &quot;Security forces&quot; &amp; actor2_bystander != 1 ~ 1,
      actor3_type == &quot;Security forces&quot; &amp; actor3_bystander != 1 ~ 1,
      actor4_type == &quot;Security forces&quot; &amp; actor4_bystander != 1 ~ 1,
      actor5_type == &quot;Security forces&quot; &amp; actor5_bystander != 1 ~ 1,
      actor6_type == &quot;Security forces&quot; &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    ),
    police_involve = case_when(
      actor1_subtype == &quot;Security forces: Police&quot; &amp; actor1_bystander != 1 ~ 1,
      actor2_subtype == &quot;Security forces: Police&quot; &amp; actor2_bystander != 1 ~ 1,
      actor3_subtype == &quot;Security forces: Police&quot; &amp; actor3_bystander != 1 ~ 1,
      actor4_subtype == &quot;Security forces: Police&quot; &amp; actor4_bystander != 1 ~ 1,
      actor5_subtype == &quot;Security forces: Police&quot; &amp; actor5_bystander != 1 ~ 1,
      actor6_subtype == &quot;Security forces: Police&quot; &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    ),
    army_involve = case_when(
      actor1_subtype == &quot;Security forces: Army&quot; &amp; actor1_bystander != 1 ~ 1,
      actor2_subtype == &quot;Security forces: Army&quot; &amp; actor2_bystander != 1 ~ 1,
      actor3_subtype == &quot;Security forces: Army&quot; &amp; actor3_bystander != 1 ~ 1,
      actor4_subtype == &quot;Security forces: Army&quot; &amp; actor4_bystander != 1 ~ 1,
      actor5_subtype == &quot;Security forces: Army&quot; &amp; actor5_bystander != 1 ~ 1,
      actor6_subtype == &quot;Security forces: Army&quot; &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    ),
    ppolice_involve = case_when(
      actor1_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor1_bystander != 1 ~ 1,
      actor2_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor2_bystander != 1 ~ 1,
      actor3_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor3_bystander != 1 ~ 1,
      actor4_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor4_bystander != 1 ~ 1,
      actor5_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor5_bystander != 1 ~ 1,
      actor6_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    )
  )

# Convert the variables to factors

maverick_sf$country &lt;- as.factor(maverick_sf$country)

maverick_sf$event_context &lt;- as.factor(maverick_sf$event_context)

maverick_sf$election &lt;- as.factor(maverick_sf$election)

# Set the reference categories 

maverick_sf$country &lt;- relevel(maverick_sf$country, ref = &quot;Kenya&quot;)

maverick_sf$event_context &lt;- relevel(maverick_sf$event_context, ref = &quot;Other context&quot;)

maverick_sf$election &lt;- relevel(maverick_sf$election, ref = &quot;CI-Presidential-1995&quot;)

# Remove events in elections with fewer than 10 violent events or for which the election is unknown

maverick_sf &lt;- maverick_sf %&gt;% 
  group_by(election) %&gt;% 
  mutate(count = n()) %&gt;% 
  ungroup() %&gt;% 
  filter(count &gt;= 10 &amp; election != &quot;Other&quot; &amp; election != &quot;Indeterminate&quot;) 

#### Table BII: Descriptive statistics ####

maverick_sf %&gt;% 
  select(
    deaths_best,
    injuries_best,
    damage,
    sf_involve,
    police_involve,
    ppolice_involve,
    army_involve,
    agri_gc,
    ttime_mean,
    event_context,
    country
  ) %&gt;%
  as.data.frame() %&gt;% 
  stargazer(
    covariate.labels = c(
      &quot;Best estimated number of deaths&quot;,
      &quot;Best estimate number of injured people&quot;,
      &quot;Material destruction&quot;,
      &quot;Security force involvement&quot;,
      &quot;Police involvement&quot;,
      &quot;Paramilitary police involvement&quot;,
      &quot;Army involvement&quot;,
      &quot;Agricultural land cover&quot;,
      &quot;Travel time to the nearest urban center&quot;
    ),
    title = &quot;Descriptive statistics&quot;,
    digits = 2,
    font.size = &quot;footnotesize&quot;,
    omit.summary.stat = c(&quot;p25&quot;, &quot;p75&quot;),
    label = &quot;tab:descriptive&quot;,
    table.placement = &quot;t&quot;,
    median = TRUE,
    type = &quot;text&quot;
    # type = &quot;latex&quot;,
    # out = &quot;table_c2.tex&quot;
  )</code></pre>
<pre><code>## 
## Descriptive statistics
## ===================================================================================
## Statistic                                 N    Mean  St. Dev.  Min  Median   Max   
## -----------------------------------------------------------------------------------
## Best estimated number of deaths         1,780  1.53    8.56     0     0      220   
## Best estimate number of injured people  1,780  1.70    8.91     0     0      200   
## Material destruction                    1,780  0.26    0.44     0     0       1    
## Security force involvement              1,780  0.46    0.50     0     0       1    
## Police involvement                      1,780  0.36    0.48     0     0       1    
## Paramilitary police involvement         1,780  0.06    0.23     0     0       1    
## Army involvement                        1,780  0.01    0.09     0     0       1    
## Agricultural land cover                 1,780 52.14   27.09   0.33  40.16   99.92  
## Travel time to the nearest urban center 1,780 162.54  110.74  89.15 114.32 1,082.55
## -----------------------------------------------------------------------------------</code></pre>
<pre class="r"><code>#### Table I: Main analysis ####

##### Model 1 ####

model1 &lt;- glm.nb(deaths_best ~ sf_involve + agri_gc + ttime_mean + election, data = maverick_sf, link = &quot;log&quot;)

# Calculate standard errors clustered by district

coeftest(model1, vcov = vcovCL(model1, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                 Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                  -1.0211e+00  6.0271e-01  -1.6942  0.090224 .  
## sf_involve                   -2.1952e-01  1.3581e-01  -1.6163  0.106020    
## agri_gc                      -3.9887e-03  4.2483e-03  -0.9389  0.347793    
## ttime_mean                    2.4225e-03  7.6505e-04   3.1665  0.001543 ** 
## electionCI-Legislative-2001   1.0638e+00  7.5363e-01   1.4116  0.158075    
## electionCI-Legislative-2011   5.2312e-01  5.9882e-01   0.8736  0.382346    
## electionCI-Municipal-2013    -3.3296e+01  6.0475e-01 -55.0578 &lt; 2.2e-16 ***
## electionCI-Municipal-2018    -7.4928e-01  6.2441e-01  -1.2000  0.230146    
## electionCI-Presidential-2000  2.0447e+00  5.5303e-01   3.6972  0.000218 ***
## electionCI-Presidential-2010  2.1541e+00  4.0213e-01   5.3566  8.48e-08 ***
## electionCI-Presidential-2020 -1.6264e-01  4.9394e-01  -0.3293  0.741942    
## electionK-General-1992        2.0931e+00  7.5787e-01   2.7617  0.005749 ** 
## electionK-General-1997        1.2882e+00  6.8491e-01   1.8808  0.060004 .  
## electionK-General-2002        6.3385e-01  6.6197e-01   0.9575  0.338304    
## electionK-General-2007        1.3669e+00  4.9384e-01   2.7679  0.005642 ** 
## electionK-General-2013        1.7510e+00  5.8162e-01   3.0106  0.002607 ** 
## electionK-General-2017       -4.0167e-01  6.1530e-01  -0.6528  0.513881    
## electionK-General-2022       -1.5240e-01  5.5705e-01  -0.2736  0.784398    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 2 ####

model2 &lt;- glm.nb(deaths_best ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_sf, link = &quot;log&quot;)

# Calculate standard errors clustered by district

coeftest(model2, vcov = vcovCL(model2, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                 Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                  -1.2091e+00  5.4713e-01  -2.2098 0.0271160 *  
## police_involve               -4.0746e-01  9.2956e-02  -4.3833 1.169e-05 ***
## ppolice_involve               8.0989e-01  1.4414e-01   5.6189 1.922e-08 ***
## army_involve                 -2.4564e-02  8.2456e-01  -0.0298 0.9762342    
## agri_gc                      -2.8217e-03  4.1861e-03  -0.6740 0.5002812    
## ttime_mean                    2.4371e-03  7.7797e-04   3.1327 0.0017320 ** 
## electionCI-Legislative-2001   4.1223e-01  7.3337e-01   0.5621 0.5740445    
## electionCI-Legislative-2011   6.1599e-01  5.9331e-01   1.0382 0.2991593    
## electionCI-Municipal-2013    -3.5981e+01  6.2490e-01 -57.5791 &lt; 2.2e-16 ***
## electionCI-Municipal-2018    -6.8403e-01  5.9031e-01  -1.1588 0.2465491    
## electionCI-Presidential-2000  1.5366e+00  5.4987e-01   2.7944 0.0051998 ** 
## electionCI-Presidential-2010  2.2066e+00  3.8581e-01   5.7194 1.069e-08 ***
## electionCI-Presidential-2020 -6.4035e-02  4.7250e-01  -0.1355 0.8921970    
## electionK-General-1992        2.2059e+00  7.3684e-01   2.9938 0.0027555 ** 
## electionK-General-1997        1.2975e+00  6.4270e-01   2.0188 0.0435063 *  
## electionK-General-2002        7.7555e-01  6.4837e-01   1.1962 0.2316323    
## electionK-General-2007        1.5200e+00  4.6604e-01   3.2615 0.0011082 ** 
## electionK-General-2013        1.9331e+00  5.5495e-01   3.4834 0.0004951 ***
## electionK-General-2017       -2.1807e-01  6.1176e-01  -0.3565 0.7214960    
## electionK-General-2022        3.8477e-03  5.3196e-01   0.0072 0.9942288    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 3 ####

model3 &lt;- glm.nb(injuries_best ~ sf_involve + agri_gc + ttime_mean + election, data = maverick_sf, link = &quot;log&quot;)

# Calculate standard errors clustered by district

coeftest(model3, vcov = vcovCL(model3, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                Estimate Std. Error z value Pr(&gt;|z|)   
## (Intercept)                   1.5185748  0.8113419  1.8717  0.06125 . 
## sf_involve                   -0.0704573  0.2918624 -0.2414  0.80924   
## agri_gc                      -0.0132232  0.0048644 -2.7184  0.00656 **
## ttime_mean                    0.0019207  0.0013800  1.3918  0.16398   
## electionCI-Legislative-2001  -0.4178295  0.8955597 -0.4666  0.64082   
## electionCI-Legislative-2011  -2.1973986  0.8444075 -2.6023  0.00926 **
## electionCI-Municipal-2013    -1.0168090  0.7624249 -1.3337  0.18232   
## electionCI-Municipal-2018    -1.6745103  0.8262400 -2.0267  0.04270 * 
## electionCI-Presidential-2000  1.1309676  0.8038253  1.4070  0.15943   
## electionCI-Presidential-2010 -0.7235508  0.7760761 -0.9323  0.35117   
## electionCI-Presidential-2020 -1.1712047  0.7589721 -1.5431  0.12280   
## electionK-General-1992        0.4289928  1.0047406  0.4270  0.66940   
## electionK-General-1997       -0.1591102  0.8722727 -0.1824  0.85526   
## electionK-General-2002       -0.1498376  0.8049842 -0.1861  0.85234   
## electionK-General-2007       -1.5770153  0.7608402 -2.0727  0.03820 * 
## electionK-General-2013       -0.0867851  1.0813693 -0.0803  0.93603   
## electionK-General-2017       -0.7001199  0.7639506 -0.9164  0.35943   
## electionK-General-2022       -1.2840317  0.8484101 -1.5135  0.13016   
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 4 ####

model4 &lt;- glm.nb(injuries_best ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_sf, link = &quot;log&quot;)

# Calculate standard errors clustered by district

coeftest(model4, vcov = vcovCL(model4, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                Estimate Std. Error z value Pr(&gt;|z|)   
## (Intercept)                   1.5413446  0.7668050  2.0101 0.044422 * 
## police_involve               -0.1329418  0.3171230 -0.4192 0.675061   
## ppolice_involve               0.2732326  0.3666838  0.7451 0.456184   
## army_involve                 -0.2066428  0.5737876 -0.3601 0.718744   
## agri_gc                      -0.0131188  0.0047212 -2.7787 0.005458 **
## ttime_mean                    0.0018819  0.0013709  1.3728 0.169821   
## electionCI-Legislative-2001  -0.6990983  0.9736872 -0.7180 0.472763   
## electionCI-Legislative-2011  -2.2145774  0.8420377 -2.6300 0.008538 **
## electionCI-Municipal-2013    -1.1026641  0.7604418 -1.4500 0.147050   
## electionCI-Municipal-2018    -1.7012384  0.8164377 -2.0837 0.037184 * 
## electionCI-Presidential-2000  1.0266193  0.8421070  1.2191 0.222803   
## electionCI-Presidential-2010 -0.7621168  0.7653888 -0.9957 0.319384   
## electionCI-Presidential-2020 -1.1961913  0.7497632 -1.5954 0.110617   
## electionK-General-1992        0.1745440  0.9717853  0.1796 0.857457   
## electionK-General-1997       -0.2059753  0.8418786 -0.2447 0.806718   
## electionK-General-2002       -0.1586955  0.7728210 -0.2053 0.837302   
## electionK-General-2007       -1.5758790  0.7432088 -2.1204 0.033975 * 
## electionK-General-2013       -0.1153312  1.0777366 -0.1070 0.914779   
## electionK-General-2017       -0.6927123  0.8291149 -0.8355 0.403445   
## electionK-General-2022       -1.2893933  0.8241966 -1.5644 0.117718   
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 5 ####

model5 &lt;- glm(damage ~ sf_involve + agri_gc + ttime_mean + election, data = maverick_sf, family = &quot;binomial&quot;)

# Calculate standard errors clustered by district

coeftest(model5, vcov = vcovCL(model5, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                 Estimate  Std. Error z value  Pr(&gt;|z|)    
## (Intercept)                  -1.59594759  0.44544693 -3.5828 0.0003399 ***
## sf_involve                   -0.82173619  0.15014718 -5.4729 4.428e-08 ***
## agri_gc                       0.00574484  0.00306138  1.8766 0.0605795 .  
## ttime_mean                    0.00158727  0.00056012  2.8338 0.0045996 ** 
## electionCI-Legislative-2001   0.79374848  1.02069962  0.7777 0.4367746    
## electionCI-Legislative-2011  -0.72750446  0.97058300 -0.7496 0.4535233    
## electionCI-Municipal-2013     0.44548339  0.55952747  0.7962 0.4259287    
## electionCI-Municipal-2018     1.02616675  0.71179476  1.4417 0.1493980    
## electionCI-Presidential-2000 -0.95842535  0.36363803 -2.6357 0.0083974 ** 
## electionCI-Presidential-2010 -0.57499250  0.43904404 -1.3096 0.1903154    
## electionCI-Presidential-2020  1.63444685  0.53528626  3.0534 0.0022626 ** 
## electionK-General-1992       -0.15186106  0.67101967 -0.2263 0.8209573    
## electionK-General-1997        0.40288340  0.54299933  0.7420 0.4581120    
## electionK-General-2002       -0.89746289  0.56281671 -1.5946 0.1108036    
## electionK-General-2007        0.97988535  0.41989350  2.3337 0.0196139 *  
## electionK-General-2013       -0.39745334  0.73907603 -0.5378 0.5907354    
## electionK-General-2017       -0.57015716  0.47173769 -1.2086 0.2268043    
## electionK-General-2022        0.31979903  0.61613841  0.5190 0.6037345    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 6 ####

model6 &lt;- glm(damage ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_sf, family = &quot;binomial&quot;)

# Calculate standard errors clustered by district

coeftest(model6, vcov = vcovCL(model6, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                 Estimate  Std. Error z value  Pr(&gt;|z|)    
## (Intercept)                  -1.91745065  0.43620275 -4.3958 1.104e-05 ***
## police_involve               -0.78220944  0.13146493 -5.9499 2.682e-09 ***
## ppolice_involve              -0.18505205  0.35286089 -0.5244 0.5999771    
## army_involve                 -1.80457519  1.32313514 -1.3639 0.1726107    
## agri_gc                       0.00591667  0.00303652  1.9485 0.0513543 .  
## ttime_mean                    0.00169908  0.00057584  2.9506 0.0031716 ** 
## electionCI-Legislative-2001   0.77022520  1.13593691  0.6781 0.4977382    
## electionCI-Legislative-2011  -0.41595885  0.95703093 -0.4346 0.6638276    
## electionCI-Municipal-2013     0.50996283  0.55168371  0.9244 0.3552909    
## electionCI-Municipal-2018     1.19406474  0.72180420  1.6543 0.0980711 .  
## electionCI-Presidential-2000 -0.84532320  0.42552883 -1.9865 0.0469752 *  
## electionCI-Presidential-2010 -0.39791589  0.44487668 -0.8944 0.3710860    
## electionCI-Presidential-2020  1.89369977  0.52950674  3.5763 0.0003484 ***
## electionK-General-1992        0.09831770  0.67241257  0.1462 0.8837506    
## electionK-General-1997        0.68975059  0.54053199  1.2761 0.2019347    
## electionK-General-2002       -0.61546054  0.55404337 -1.1109 0.2666318    
## electionK-General-2007        1.25979416  0.41585506  3.0294 0.0024503 ** 
## electionK-General-2013       -0.13473365  0.72317636 -0.1863 0.8522031    
## electionK-General-2017       -0.33257832  0.46220702 -0.7195 0.4718058    
## electionK-General-2022        0.60089313  0.60839112  0.9877 0.3233115    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code># Create lists of clustered standard errors

cluster_se1 &lt;- sqrt(diag(vcovCL(model1, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se2 &lt;- sqrt(diag(vcovCL(model2, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se3 &lt;- sqrt(diag(vcovCL(model3, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se4 &lt;- sqrt(diag(vcovCL(model4, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se5 &lt;- sqrt(diag(vcovCL(model5, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se6 &lt;- sqrt(diag(vcovCL(model6, type = &quot;HC&quot;, cluster = ~ district)))

# Format additional lines

additional_lines &lt;- list(
  c(&quot;Election fixed effects&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;),
  c(&quot;Aggregation&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;)
)

##### Make regression table ####

stargazer(
  model1,
  model2,
  model3,
  model4,
  model5,
  model6,
  digits = 2,
  single.row = FALSE,
  se = list(cluster_se1, cluster_se2, cluster_se3, cluster_se4, cluster_se5, cluster_se6),
  dep.var.labels = c(&quot;Lethal violence&quot;, &quot;Injurious violence&quot;, &quot;Material destruction&quot;),
  model.names = FALSE,
  covariate.labels = c(
    &quot;Security force involvement&quot;,
    &quot;Police involvement&quot;,
    &quot;Paramilitary police involvement&quot;,
    &quot;Army involvement&quot;,
    &quot;Agricultural land cover&quot;,
    &quot;Travel time to nearest urban center&quot;
  ),
  omit = c(&quot;election&quot;, &quot;Constant&quot;),
  title = &quot;Regression of security force involvement and electoral violence&quot;,
  label = &quot;tab:regression&quot;,
  table.placement = &quot;t&quot;,
  add.lines = additional_lines,
  font.size = &quot;scriptsize&quot;,
  notes.append = FALSE,
  star.char = c(&quot;+&quot;, &quot;*&quot;, &quot;**&quot;),
  notes = c(&quot;Robust standard errors in parentheses, clustered by district/county:&quot;, &quot;+ p $&lt;$ 0.1, * p $&lt;$ 0.05, ** p $&lt;$ 0.01&quot;),
  type = &quot;text&quot;
  # type = &quot;latex&quot;,
  # out = &quot;table_1.tex&quot;
)</code></pre>
<pre><code>## 
## Regression of security force involvement and electoral violence
## ===============================================================================================================
##                                                                 Dependent variable:                            
##                                     ---------------------------------------------------------------------------
##                                           Lethal violence           Injurious violence      Material destruction
##                                          (1)           (2)           (3)           (4)         (5)       (6)   
## ---------------------------------------------------------------------------------------------------------------
## Security force involvement              -0.22                       -0.07                    -0.82**           
##                                        (0.14)                      (0.29)                    (0.15)            
##                                                                                                                
## Police involvement                                   -0.41**                      -0.13                -0.78** 
##                                                      (0.09)                      (0.32)                (0.13)  
##                                                                                                                
## Paramilitary police involvement                      0.81**                       0.27                  -0.19  
##                                                      (0.14)                      (0.37)                (0.35)  
##                                                                                                                
## Army involvement                                      -0.02                       -0.21                 -1.80  
##                                                      (0.82)                      (0.57)                (1.32)  
##                                                                                                                
## Agricultural land cover                -0.004        -0.003        -0.01**       -0.01**      0.01+     0.01+  
##                                        (0.004)       (0.004)       (0.005)       (0.005)     (0.003)   (0.003) 
##                                                                                                                
## Travel time to nearest urban center    0.002**       0.002**        0.002         0.002      0.002**   0.002** 
##                                        (0.001)       (0.001)       (0.001)       (0.001)     (0.001)   (0.001) 
##                                                                                                                
## ---------------------------------------------------------------------------------------------------------------
## Election fixed effects                   Yes           Yes           Yes           Yes         Yes       Yes   
## Aggregation                           Most-rep.     Most-rep.     Most-rep.     Most-rep.   Most-rep. Most-rep.
## Observations                            1,780         1,780         1,780         1,780       1,780     1,780  
## Log Likelihood                        -2,128.87     -2,121.83     -2,422.71     -2,421.80    -835.36   -837.26 
## theta                               0.23** (0.01) 0.24** (0.01) 0.22** (0.01) 0.22** (0.01)                    
## Akaike Inf. Crit.                     4,293.74      4,283.65      4,881.43      4,883.60    1,706.71  1,714.53 
## ===============================================================================================================
## Note:                                      Robust standard errors in parentheses, clustered by district/county:
##                                                                              + p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01</code></pre>
<pre class="r"><code>##### Test that the difference in coefficients is statistically significant ####

vcov_cluster &lt;- vcovCL(model2, cluster = ~district)

# Test if police_involve = ppolice_involve

linearHypothesis(model2, &quot;police_involve = ppolice_involve&quot;, vcov. = vcov_cluster)</code></pre>
<pre><code>## 
## Linear hypothesis test:
## police_involve - ppolice_involve = 0
## 
## Model 1: restricted model
## Model 2: deaths_best ~ police_involve + ppolice_involve + army_involve + 
##     agri_gc + ttime_mean + election
## 
## Note: Coefficient covariance matrix supplied.
## 
##   Res.Df Df  Chisq Pr(&gt;Chisq)    
## 1   1761                         
## 2   1760  1 83.059  &lt; 2.2e-16 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code># Test if police_involve = army_involve

linearHypothesis(model2, &quot;police_involve = army_involve&quot;, vcov. = vcov_cluster)</code></pre>
<pre><code>## 
## Linear hypothesis test:
## police_involve - army_involve = 0
## 
## Model 1: restricted model
## Model 2: deaths_best ~ police_involve + ppolice_involve + army_involve + 
##     agri_gc + ttime_mean + election
## 
## Note: Coefficient covariance matrix supplied.
## 
##   Res.Df Df  Chisq Pr(&gt;Chisq)
## 1   1761                     
## 2   1760  1 0.2127     0.6447</code></pre>
<pre class="r"><code>##### Predicted values ####

# Predicted reduction in deaths with police involvement

predict_model2 &lt;- predictions(
  model2, 
  newdata = datagrid(
    police_involve = c(0,1)
  ),
  by = &quot;police_involve&quot;,
  vcov = sandwich::vcovCL(model2, cluster = ~ district)
)

predicted_value1 &lt;- predict_model2 %&gt;% 
  as.data.frame() %&gt;% 
  filter(police_involve == 0) %&gt;% 
  mutate(estimate = round(estimate, 2)) %&gt;% 
  pull(estimate)

predicted_value2 &lt;- predict_model2 %&gt;% 
  as.data.frame() %&gt;% 
  filter(police_involve == 1) %&gt;% 
  mutate(estimate = round(estimate, 2)) %&gt;% 
  pull(estimate)

predicted_value1</code></pre>
<pre><code>## [1] 0.31</code></pre>
<pre class="r"><code>predicted_value2</code></pre>
<pre><code>## [1] 0.2</code></pre>
<pre class="r"><code>round((predicted_value2-predicted_value1)/predicted_value1 * -100, 0)</code></pre>
<pre><code>## [1] 35</code></pre>
<pre class="r"><code>round(mean(maverick_sf$deaths_best), 2)</code></pre>
<pre><code>## [1] 1.53</code></pre>
<pre class="r"><code>median(maverick_sf$deaths_best)</code></pre>
<pre><code>## [1] 0</code></pre>
<pre class="r"><code># Predicted increase in deaths with paramilitary police involvement

predict_model2 &lt;- predictions(
  model2,
  newdata = datagrid(
    ppolice_involve = c(0,1)
  ),
  by = &quot;ppolice_involve&quot;,
  vcov = sandwich::vcovCL(model4, cluster = ~ country_year)
)

predicted_value3 &lt;- predict_model2 %&gt;%
  as.data.frame() %&gt;%
  filter(ppolice_involve == 0) %&gt;%
  mutate(estimate = round(estimate, 2)) %&gt;%
  pull(estimate)

predicted_value4 &lt;- predict_model2 %&gt;%
  as.data.frame() %&gt;%
  filter(ppolice_involve == 1) %&gt;%
  mutate(estimate = round(estimate, 2)) %&gt;%
  pull(estimate)

predicted_value3</code></pre>
<pre><code>## [1] 0.31</code></pre>
<pre class="r"><code>predicted_value4</code></pre>
<pre><code>## [1] 0.69</code></pre>
<pre class="r"><code>round((predicted_value4-predicted_value3)/predicted_value3 * 100, 0)</code></pre>
<pre><code>## [1] 123</code></pre>
<pre class="r"><code># Predicted reduction in the risk of material destruction 

predict_model6 &lt;- predictions(
  model6, 
  newdata = datagrid(
    police_involve = c(0,1)
  ),
  by = &quot;police_involve&quot;,
  vcov = sandwich::vcovCL(model6, cluster = ~ district)
)

predict_model6 %&gt;% 
  as.data.frame() %&gt;% 
  filter(police_involve == 0) %&gt;% 
  mutate(estimate = round(estimate * 100, 0)) %&gt;% 
  pull(estimate)</code></pre>
<pre><code>## [1] 16</code></pre>
<pre class="r"><code>predict_model6 %&gt;% 
  as.data.frame() %&gt;% 
  filter(police_involve == 1) %&gt;% 
  mutate(estimate = round(estimate * 100, 0)) %&gt;% 
  pull(estimate)</code></pre>
<pre><code>## [1] 8</code></pre>
<pre class="r"><code>##### Clean working environment ####

rm(
  model1, model2, model3, model4, model5, model6,
  cluster_se1, cluster_se2, cluster_se3, cluster_se4, cluster_se5, cluster_se6,
  additional_lines, predict_model2, predict_model6,
  predicted_value1, predicted_value2, predicted_value3, 
  predicted_value4, vcov_cluster
)

#### Accounting for aggregation sensitivity ####

##### Prepare most-informative dataset ####

# Make dataset spatial

maverick_sf_inf &lt;- maverick_inf %&gt;%
  filter(geo_precision &gt; 2 &amp; !is.na(latitude) &amp; !is.na(longitude) &amp; latitude != 0 &amp; longitude != 0) %&gt;% 
  mutate(year = year(as.Date(date_start, format = &quot;%Y-%m-%d&quot;))) %&gt;% 
  mutate(country_year = paste(country, year, sep = &quot;_&quot;)) %&gt;% 
  st_as_sf(coords = c(&quot;longitude&quot;, &quot;latitude&quot;), crs = 4326, remove = FALSE)

# Add district names from shapefiles

maverick_sf_inf &lt;- maverick_sf_inf %&gt;%
  st_join(shapefile_civ %&gt;% select(ADM1_FR), left = TRUE) %&gt;% 
  st_join(shapefile_ke %&gt;% select(shapeName), left = TRUE) %&gt;% 
  mutate(
    district = case_when(
      country == &quot;Ivory Coast&quot; ~ ADM1_FR,
      TRUE ~ shapeName
    )
  ) %&gt;%
  filter(!is.na(district)) %&gt;% 
  select(-ADM1_FR, -shapeName)

# Join dataset with PRIO-Grid dataset

maverick_sf_inf &lt;- maverick_sf_inf %&gt;% 
  st_join(shapefile_prio_grid)

# Create variables for analysis 

maverick_sf_inf &lt;- maverick_sf_inf %&gt;% 
  # Create security force involvement variable
  mutate(
    sf_involve = case_when(
      actor1_type == &quot;Security forces&quot; &amp; actor1_bystander != 1 ~ 1,
      actor2_type == &quot;Security forces&quot; &amp; actor2_bystander != 1 ~ 1,
      actor3_type == &quot;Security forces&quot; &amp; actor3_bystander != 1 ~ 1,
      actor4_type == &quot;Security forces&quot; &amp; actor4_bystander != 1 ~ 1,
      actor5_type == &quot;Security forces&quot; &amp; actor5_bystander != 1 ~ 1,
      actor6_type == &quot;Security forces&quot; &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    ),
    police_involve = case_when(
      actor1_subtype == &quot;Security forces: Police&quot; &amp; actor1_bystander != 1 ~ 1,
      actor2_subtype == &quot;Security forces: Police&quot; &amp; actor2_bystander != 1 ~ 1,
      actor3_subtype == &quot;Security forces: Police&quot; &amp; actor3_bystander != 1 ~ 1,
      actor4_subtype == &quot;Security forces: Police&quot; &amp; actor4_bystander != 1 ~ 1,
      actor5_subtype == &quot;Security forces: Police&quot; &amp; actor5_bystander != 1 ~ 1,
      actor6_subtype == &quot;Security forces: Police&quot; &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    ),
    army_involve = case_when(
      actor1_subtype == &quot;Security forces: Army&quot; &amp; actor1_bystander != 1 ~ 1,
      actor2_subtype == &quot;Security forces: Army&quot; &amp; actor2_bystander != 1 ~ 1,
      actor3_subtype == &quot;Security forces: Army&quot; &amp; actor3_bystander != 1 ~ 1,
      actor4_subtype == &quot;Security forces: Army&quot; &amp; actor4_bystander != 1 ~ 1,
      actor5_subtype == &quot;Security forces: Army&quot; &amp; actor5_bystander != 1 ~ 1,
      actor6_subtype == &quot;Security forces: Army&quot; &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    ),
    ppolice_involve = case_when(
      actor1_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor1_bystander != 1 ~ 1,
      actor2_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor2_bystander != 1 ~ 1,
      actor3_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor3_bystander != 1 ~ 1,
      actor4_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor4_bystander != 1 ~ 1,
      actor5_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor5_bystander != 1 ~ 1,
      actor6_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    )
  )

# Convert the variables to factors

maverick_sf_inf$election &lt;- as.factor(maverick_sf_inf$election)

# Set the reference categories 

maverick_sf_inf$election &lt;- relevel(maverick_sf_inf$election, ref = &quot;CI-Presidential-1995&quot;)

##### Prepare most-conservative dataset ####

# Make dataset spatial

maverick_sf_con &lt;- maverick_con %&gt;%
  filter(geo_precision &gt; 2 &amp; !is.na(latitude) &amp; !is.na(longitude) &amp; latitude != 0 &amp; longitude != 0) %&gt;% 
  mutate(year = year(as.Date(date_start, format = &quot;%Y-%m-%d&quot;))) %&gt;% 
  mutate(country_year = paste(country, year, sep = &quot;_&quot;)) %&gt;% 
  st_as_sf(coords = c(&quot;longitude&quot;, &quot;latitude&quot;), crs = 4326, remove = FALSE)

# Add district names from shapefiles

maverick_sf_con &lt;- maverick_sf_con %&gt;%
  st_join(shapefile_civ %&gt;% select(ADM1_FR), left = TRUE) %&gt;% 
  st_join(shapefile_ke %&gt;% select(shapeName), left = TRUE) %&gt;% 
  mutate(
    district = case_when(
      country == &quot;Ivory Coast&quot; ~ ADM1_FR,
      TRUE ~ shapeName
    )
  ) %&gt;%
  filter(!is.na(district)) %&gt;% 
  select(-ADM1_FR, -shapeName)

# Join dataset with PRIO-Grid dataset

maverick_sf_con &lt;- maverick_sf_con %&gt;% 
  st_join(shapefile_prio_grid)

# Create variables for analysis 

maverick_sf_con &lt;- maverick_sf_con %&gt;% 
  # Create security force involvement variable
  mutate(
    sf_involve = case_when(
      actor1_type == &quot;Security forces&quot; &amp; actor1_bystander != 1 ~ 1,
      actor2_type == &quot;Security forces&quot; &amp; actor2_bystander != 1 ~ 1,
      actor3_type == &quot;Security forces&quot; &amp; actor3_bystander != 1 ~ 1,
      actor4_type == &quot;Security forces&quot; &amp; actor4_bystander != 1 ~ 1,
      actor5_type == &quot;Security forces&quot; &amp; actor5_bystander != 1 ~ 1,
      actor6_type == &quot;Security forces&quot; &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    ),
    police_involve = case_when(
      actor1_subtype == &quot;Security forces: Police&quot; &amp; actor1_bystander != 1 ~ 1,
      actor2_subtype == &quot;Security forces: Police&quot; &amp; actor2_bystander != 1 ~ 1,
      actor3_subtype == &quot;Security forces: Police&quot; &amp; actor3_bystander != 1 ~ 1,
      actor4_subtype == &quot;Security forces: Police&quot; &amp; actor4_bystander != 1 ~ 1,
      actor5_subtype == &quot;Security forces: Police&quot; &amp; actor5_bystander != 1 ~ 1,
      actor6_subtype == &quot;Security forces: Police&quot; &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    ),
    army_involve = case_when(
      actor1_subtype == &quot;Security forces: Army&quot; &amp; actor1_bystander != 1 ~ 1,
      actor2_subtype == &quot;Security forces: Army&quot; &amp; actor2_bystander != 1 ~ 1,
      actor3_subtype == &quot;Security forces: Army&quot; &amp; actor3_bystander != 1 ~ 1,
      actor4_subtype == &quot;Security forces: Army&quot; &amp; actor4_bystander != 1 ~ 1,
      actor5_subtype == &quot;Security forces: Army&quot; &amp; actor5_bystander != 1 ~ 1,
      actor6_subtype == &quot;Security forces: Army&quot; &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    ),
    ppolice_involve = case_when(
      actor1_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor1_bystander != 1 ~ 1,
      actor2_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor2_bystander != 1 ~ 1,
      actor3_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor3_bystander != 1 ~ 1,
      actor4_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor4_bystander != 1 ~ 1,
      actor5_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor5_bystander != 1 ~ 1,
      actor6_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    )
  )

# Convert the variables to factors

maverick_sf_con$election &lt;- as.factor(maverick_sf_con$election)

# Set the reference categories 

maverick_sf_con$election &lt;- relevel(maverick_sf_con$election, ref = &quot;CI-Presidential-1995&quot;)

##### Model 7 ####

model7 &lt;- glm.nb(deaths_best ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_sf_inf, link = &quot;log&quot;)

# Calculate standard errors clustered by district

coeftest(model7, vcov = vcovCL(model7, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                  Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                   -8.8168e-01  4.2706e-01  -2.0645 0.0389678 *  
## police_involve                -1.2125e-01  1.1077e-01  -1.0947 0.2736683    
## ppolice_involve                7.2467e-01  1.4225e-01   5.0944 3.498e-07 ***
## army_involve                   5.5712e-01  2.6580e-01   2.0960 0.0360792 *  
## agri_gc                       -9.0104e-04  5.5093e-03  -0.1635 0.8700878    
## ttime_mean                     2.4084e-03  8.0604e-04   2.9880 0.0028085 ** 
## election                       1.0124e+00  3.4918e-01   2.8993 0.0037399 ** 
## electionCI-Legislative-1995   -3.6783e+01  7.1855e-01 -51.1907 &lt; 2.2e-16 ***
## electionCI-Legislative-2001    2.0903e-01  5.5464e-01   0.3769 0.7062702    
## electionCI-Legislative-2011    1.6991e-01  5.6017e-01   0.3033 0.7616508    
## electionCI-Legislative-2016   -3.6666e+01  9.2631e-01 -39.5825 &lt; 2.2e-16 ***
## electionCI-Legislative-2021   -3.6372e+01  9.3743e-01 -38.7999 &lt; 2.2e-16 ***
## electionCI-Local-2002          1.0941e+00  3.6667e-01   2.9838 0.0028469 ** 
## electionCI-Municipal-2001      1.2848e-01  4.8503e-01   0.2649 0.7910993    
## electionCI-Municipal-2013     -2.1977e+00  4.1595e-01  -5.2836 1.266e-07 ***
## electionCI-Municipal-2018     -1.2556e+00  4.7267e-01  -2.6564 0.0078975 ** 
## electionCI-Presidential-2000   1.4877e+00  3.1747e-01   4.6861 2.784e-06 ***
## electionCI-Presidential-2010   2.3307e+00  5.1971e-01   4.4846 7.304e-06 ***
## electionCI-Presidential-2015  -2.5980e-01  8.6969e-01  -0.2987 0.7651531    
## electionCI-Presidential-2020   2.5762e-02  4.3614e-01   0.0591 0.9528971    
## electionCI-Senatorial-2018    -3.6251e+01  9.4762e-01 -38.2547 &lt; 2.2e-16 ***
## electionIndeterminate          1.7692e+00  3.0201e-01   5.8583 4.676e-09 ***
## electionK-Constitutional-2005 -3.6373e+01  1.1309e+00 -32.1626 &lt; 2.2e-16 ***
## electionK-Constitutional-2010  2.2801e+00  3.0201e-01   7.5497 4.361e-14 ***
## electionK-General-1992         1.7662e+00  6.5789e-01   2.6846 0.0072622 ** 
## electionK-General-1997         1.4281e+00  4.0734e-01   3.5059 0.0004551 ***
## electionK-General-2002         5.6654e-01  5.6708e-01   0.9990 0.3177718    
## electionK-General-2007         1.5025e+00  2.8257e-01   5.3173 1.053e-07 ***
## electionK-General-2013         1.7949e+00  4.4322e-01   4.0498 5.126e-05 ***
## electionK-General-2017        -4.0047e-01  5.0164e-01  -0.7983 0.4246916    
## electionK-General-2022        -2.9028e-01  3.6554e-01  -0.7941 0.4271421    
## electionOther                 -2.3179e+00  3.9813e-01  -5.8220 5.815e-09 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 8 ####

model8 &lt;- glm.nb(deaths_best ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_sf_con, link = &quot;log&quot;)

# Calculate standard errors clustered by district

coeftest(model8, vcov = vcovCL(model8, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                  Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                   -1.1963e+00  5.5597e-01  -2.1517 0.0314235 *  
## police_involve                -5.9478e-01  1.1150e-01  -5.3343 9.593e-08 ***
## ppolice_involve                9.0156e-01  1.6558e-01   5.4450 5.180e-08 ***
## army_involve                   5.6881e-02  8.2642e-01   0.0688 0.9451261    
## agri_gc                       -1.9099e-03  4.4902e-03  -0.4253 0.6705860    
## ttime_mean                     2.2256e-03  8.2808e-04   2.6877 0.0071946 ** 
## election                       1.0293e+00  4.4273e-01   2.3250 0.0200720 *  
## electionCI-Legislative-1995   -3.6159e+01  6.6378e-01 -54.4748 &lt; 2.2e-16 ***
## electionCI-Legislative-2001    4.2385e-01  7.1383e-01   0.5938 0.5526615    
## electionCI-Legislative-2011    1.2347e-01  6.5737e-01   0.1878 0.8510101    
## electionCI-Legislative-2016   -3.6147e+01  7.8814e-01 -45.8639 &lt; 2.2e-16 ***
## electionCI-Legislative-2021   -3.5988e+01  7.7281e-01 -46.5669 &lt; 2.2e-16 ***
## electionCI-Local-2002          1.2734e+00  4.6334e-01   2.7482 0.0059923 ** 
## electionCI-Municipal-2001     -3.6620e-02  9.7813e-01  -0.0374 0.9701354    
## electionCI-Municipal-2013     -3.6007e+01  6.3571e-01 -56.6401 &lt; 2.2e-16 ***
## electionCI-Municipal-2018     -7.0400e-01  5.8743e-01  -1.1984 0.2307480    
## electionCI-Presidential-2000   1.2054e+00  5.2841e-01   2.2813 0.0225323 *  
## electionCI-Presidential-2010   2.1271e+00  3.7620e-01   5.6540 1.567e-08 ***
## electionCI-Presidential-2015  -2.5922e-01  8.0111e-01  -0.3236 0.7462608    
## electionCI-Presidential-2020  -2.4015e-01  4.6521e-01  -0.5162 0.6056996    
## electionCI-Senatorial-2018    -3.5406e+01  7.8073e-01 -45.3492 &lt; 2.2e-16 ***
## electionIndeterminate          2.1259e+00  4.8165e-01   4.4139 1.015e-05 ***
## electionK-Constitutional-2005 -3.6016e+01  1.2041e+00 -29.9124 &lt; 2.2e-16 ***
## electionK-Constitutional-2010  2.1259e+00  4.8165e-01   4.4139 1.015e-05 ***
## electionK-General-1992         2.2458e+00  7.3393e-01   3.0599 0.0022138 ** 
## electionK-General-1997         1.3191e+00  6.3459e-01   2.0787 0.0376465 *  
## electionK-General-2002         6.3255e-01  7.1200e-01   0.8884 0.3743195    
## electionK-General-2007         1.4516e+00  4.5301e-01   3.2043 0.0013538 ** 
## electionK-General-2013         1.2158e+00  4.8783e-01   2.4922 0.0126938 *  
## electionK-General-2017        -2.3495e-01  6.4000e-01  -0.3671 0.7135415    
## electionK-General-2022         1.1910e-02  5.2329e-01   0.0228 0.9818416    
## electionOther                 -2.7253e+00  8.1760e-01  -3.3333 0.0008581 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 9 ####

model9 &lt;- glm.nb(injuries_best ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_sf_inf, link = &quot;log&quot;)

# Calculate standard errors clustered by district

coeftest(model9, vcov = vcovCL(model9, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                  Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                     1.3443384   0.7929077   1.6955   0.08999 .  
## police_involve                 -0.0331183   0.3093941  -0.1070   0.91476    
## ppolice_involve                 0.5596089   0.3168360   1.7662   0.07736 .  
## army_involve                    0.2154259   0.3629147   0.5936   0.55278    
## agri_gc                        -0.0076426   0.0053397  -1.4313   0.15235    
## ttime_mean                      0.0016501   0.0015642   1.0549   0.29146    
## election                        2.1392119   0.9261506   2.3098   0.02090 *  
## electionCI-Legislative-1995    -0.4912641   0.9123273  -0.5385   0.59025    
## electionCI-Legislative-2001    -0.3981454   0.9533697  -0.4176   0.67623    
## electionCI-Legislative-2011    -1.6131815   0.8994826  -1.7935   0.07290 .  
## electionCI-Legislative-2016   -28.7694437   0.8799403 -32.6948 &lt; 2.2e-16 ***
## electionCI-Legislative-2021    -0.1207935   0.7518996  -0.1607   0.87237    
## electionCI-Local-2002           1.4737508   0.7481037   1.9700   0.04884 *  
## electionCI-Municipal-2001      -0.1228792   0.9363328  -0.1312   0.89559    
## electionCI-Municipal-2013      -0.5149636   0.7514028  -0.6853   0.49313    
## electionCI-Municipal-2018      -1.3156513   0.7445469  -1.7670   0.07722 .  
## electionCI-Presidential-2000    1.0088862   0.8620396   1.1703   0.24186    
## electionCI-Presidential-2010   -0.1292657   0.7623164  -0.1696   0.86535    
## electionCI-Presidential-2015   -0.6897107   0.6885251  -1.0017   0.31648    
## electionCI-Presidential-2020   -0.3817642   0.7328828  -0.5209   0.60243    
## electionCI-Senatorial-2018    -28.4888726   1.0019506 -28.4334 &lt; 2.2e-16 ***
## electionIndeterminate          -0.6256200   0.7684423  -0.8141   0.41556    
## electionK-Constitutional-2005  -1.3187672   0.7684423  -1.7162   0.08613 .  
## electionK-Constitutional-2010   3.0506807   0.7684423   3.9700 7.189e-05 ***
## electionK-General-1992         -0.0300293   0.9315851  -0.0322   0.97428    
## electionK-General-1997         -0.0983756   0.8669834  -0.1135   0.90966    
## electionK-General-2002          0.4220347   0.7798771   0.5412   0.58840    
## electionK-General-2007         -0.4868041   0.7677479  -0.6341   0.52604    
## electionK-General-2013          0.1933191   0.9385695   0.2060   0.83681    
## electionK-General-2017         -0.5692279   0.8273991  -0.6880   0.49147    
## electionK-General-2022         -1.3906779   0.8481594  -1.6396   0.10108    
## electionOther                   0.1211721   0.7858226   0.1542   0.87745    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 10 ####

model10 &lt;- glm.nb(injuries_best ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_sf_con, link = &quot;log&quot;)

# Calculate standard errors clustered by district

coeftest(model10, vcov = vcovCL(model10, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                  Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                     1.2830663   0.7602543   1.6877  0.091473 .  
## police_involve                  0.0846321   0.3114013   0.2718  0.785793    
## ppolice_involve                 0.4741437   0.3323860   1.4265  0.153728    
## army_involve                    0.0351327   0.6198726   0.0567  0.954802    
## agri_gc                        -0.0126537   0.0044779  -2.8258  0.004716 ** 
## ttime_mean                      0.0026929   0.0012757   2.1110  0.034775 *  
## election                        1.9030785   0.9298035   2.0468  0.040682 *  
## electionCI-Legislative-1995    -0.8950611   0.8452770  -1.0589  0.289647    
## electionCI-Legislative-2001    -0.7103248   0.9422009  -0.7539  0.450910    
## electionCI-Legislative-2011    -3.3807335   1.1064015  -3.0556  0.002246 ** 
## electionCI-Legislative-2016   -31.6444808   0.8834095 -35.8209 &lt; 2.2e-16 ***
## electionCI-Legislative-2021     0.0218764   0.7272149   0.0301  0.976001    
## electionCI-Local-2002         -30.7804989   1.1665642 -26.3856 &lt; 2.2e-16 ***
## electionCI-Municipal-2001      -1.0043016   0.6341823  -1.5836  0.113281    
## electionCI-Municipal-2013      -0.9468642   0.7526176  -1.2581  0.208357    
## electionCI-Municipal-2018      -1.6034503   0.8070599  -1.9868  0.046947 *  
## electionCI-Presidential-2000    0.9838029   0.8186852   1.2017  0.229485    
## electionCI-Presidential-2010   -0.9002660   0.7259065  -1.2402  0.214903    
## electionCI-Presidential-2015   -2.3120179   1.1643456  -1.9857  0.047069 *  
## electionCI-Presidential-2020   -1.4554095   0.7400746  -1.9666  0.049233 *  
## electionCI-Senatorial-2018    -31.4594614   0.9579277 -32.8412 &lt; 2.2e-16 ***
## electionIndeterminate          -0.5437996   0.7394809  -0.7354  0.462108    
## electionK-Constitutional-2005  -1.2369468   0.7394809  -1.6727  0.094382 .  
## electionK-Constitutional-2010 -31.5395318   1.2070798 -26.1288 &lt; 2.2e-16 ***
## electionK-General-1992          0.1709466   0.9399153   0.1819  0.855681    
## electionK-General-1997         -0.2673265   0.8222365  -0.3251  0.745089    
## electionK-General-2002         -0.0904502   0.7664886  -0.1180  0.906063    
## electionK-General-2007         -1.5766225   0.7297062  -2.1606  0.030724 *  
## electionK-General-2013          0.0530190   1.0359221   0.0512  0.959182    
## electionK-General-2017         -0.7501901   0.8073443  -0.9292  0.352782    
## electionK-General-2022         -1.2211894   0.8272749  -1.4762  0.139901    
## electionOther                  -0.0022279   0.7690563  -0.0029  0.997689    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 11 ####

model11 &lt;- glm(damage ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_sf_inf, family = &quot;binomial&quot;)

# Calculate standard errors clustered by district

coeftest(model11, vcov = vcovCL(model11, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                  Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                   -1.7883e+00  4.1253e-01  -4.3351 1.457e-05 ***
## police_involve                -4.8550e-01  1.2360e-01  -3.9280 8.565e-05 ***
## ppolice_involve               -2.4982e-01  2.7146e-01  -0.9203 0.3574220    
## army_involve                  -5.1593e-01  2.9659e-01  -1.7395 0.0819442 .  
## agri_gc                        3.8925e-03  2.7201e-03   1.4310 0.1524330    
## ttime_mean                     1.3871e-03  5.6368e-04   2.4609 0.0138590 *  
## election                       1.3345e+00  1.3755e+00   0.9702 0.3319364    
## electionCI-Legislative-1995    1.5525e+00  1.3342e+00   1.1636 0.2445833    
## electionCI-Legislative-2001    7.3249e-01  1.0831e+00   0.6763 0.4988741    
## electionCI-Legislative-2011    1.9713e-02  8.2513e-01   0.0239 0.9809399    
## electionCI-Legislative-2016    1.8750e+00  4.4324e-01   4.2302 2.334e-05 ***
## electionCI-Legislative-2021   -1.3068e+01  1.1806e+00 -11.0689 &lt; 2.2e-16 ***
## electionCI-Local-2002          1.3353e+00  3.8991e-01   3.4246 0.0006158 ***
## electionCI-Municipal-2001      4.6734e-01  1.1367e+00   0.4111 0.6809640    
## electionCI-Municipal-2013      1.4958e+00  5.7406e-01   2.6056 0.0091703 ** 
## electionCI-Municipal-2018      1.4136e+00  6.5285e-01   2.1652 0.0303703 *  
## electionCI-Presidential-2000  -2.9945e-01  3.9712e-01  -0.7540 0.4508292    
## electionCI-Presidential-2010  -1.0182e-01  4.0056e-01  -0.2542 0.7993447    
## electionCI-Presidential-2015   1.2567e+00  4.5012e-01   2.7920 0.0052389 ** 
## electionCI-Presidential-2020   2.0348e+00  5.1781e-01   3.9296 8.508e-05 ***
## electionCI-Senatorial-2018    -1.2582e+01  1.2181e+00 -10.3296 &lt; 2.2e-16 ***
## electionIndeterminate         -1.3006e+01  1.0161e+00 -12.8005 &lt; 2.2e-16 ***
## electionK-Constitutional-2005 -1.3006e+01  1.0161e+00 -12.8005 &lt; 2.2e-16 ***
## electionK-Constitutional-2010 -1.3006e+01  1.0161e+00 -12.8005 &lt; 2.2e-16 ***
## electionK-General-1992         1.0897e-01  6.6439e-01   0.1640 0.8697209    
## electionK-General-1997         6.7147e-01  4.9903e-01   1.3456 0.1784447    
## electionK-General-2002        -4.6234e-01  5.3880e-01  -0.8581 0.3908366    
## electionK-General-2007         1.3127e+00  4.0287e-01   3.2583 0.0011208 ** 
## electionK-General-2013         3.9206e-01  5.8256e-01   0.6730 0.5009427    
## electionK-General-2017        -3.5707e-01  4.4506e-01  -0.8023 0.4223737    
## electionK-General-2022         4.3525e-01  5.7071e-01   0.7626 0.4456782    
## electionOther                 -6.8710e-01  4.8091e-01  -1.4288 0.1530747    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 12 ####

model12 &lt;- glm(damage ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_sf_con, family = &quot;binomial&quot;)

# Calculate standard errors clustered by district

coeftest(model12, vcov = vcovCL(model12, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                  Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                   -2.3244e+00  5.2063e-01  -4.4646 8.021e-06 ***
## police_involve                -6.3845e-01  1.5044e-01  -4.2439 2.197e-05 ***
## ppolice_involve               -8.7747e-02  3.8026e-01  -0.2308 0.8175052    
## army_involve                  -1.5680e+00  1.3014e+00  -1.2048 0.2282624    
## agri_gc                        6.5691e-03  3.1748e-03   2.0691 0.0385335 *  
## ttime_mean                     1.6788e-03  6.2815e-04   2.6726 0.0075271 ** 
## election                      -1.2937e+01  8.4311e-01 -15.3449 &lt; 2.2e-16 ***
## electionCI-Legislative-1995    2.0282e+00  1.1866e+00   1.7092 0.0874067 .  
## electionCI-Legislative-2001    8.8478e-01  9.8048e-01   0.9024 0.3668453    
## electionCI-Legislative-2011   -5.4884e-02  9.4000e-01  -0.0584 0.9534400    
## electionCI-Legislative-2016    2.2781e+00  5.2748e-01   4.3189 1.568e-05 ***
## electionCI-Legislative-2021   -1.2638e+01  1.0588e+00 -11.9366 &lt; 2.2e-16 ***
## electionCI-Local-2002          3.5529e-01  4.3653e-01   0.8139 0.4157125    
## electionCI-Municipal-2001     -9.6094e-02  1.3211e+00  -0.0727 0.9420154    
## electionCI-Municipal-2013      3.2808e-02  8.0776e-01   0.0406 0.9676017    
## electionCI-Municipal-2018      1.2206e+00  7.4450e-01   1.6395 0.1011107    
## electionCI-Presidential-2000  -9.5485e-01  5.4350e-01  -1.7568 0.0789443 .  
## electionCI-Presidential-2010  -1.3643e-01  4.6329e-01  -0.2945 0.7683848    
## electionCI-Presidential-2015   7.1262e-01  1.2463e+00   0.5718 0.5674662    
## electionCI-Presidential-2020   2.0032e+00  5.1547e-01   3.8861 0.0001019 ***
## electionCI-Senatorial-2018    -1.2025e+01  1.0292e+00 -11.6838 &lt; 2.2e-16 ***
## electionIndeterminate         -1.2563e+01  1.0157e+00 -12.3692 &lt; 2.2e-16 ***
## electionK-Constitutional-2005 -1.2563e+01  1.0157e+00 -12.3692 &lt; 2.2e-16 ***
## electionK-Constitutional-2010 -1.2563e+01  1.0157e+00 -12.3692 &lt; 2.2e-16 ***
## electionK-General-1992         4.6346e-01  7.0045e-01   0.6617 0.5081905    
## electionK-General-1997         7.4049e-01  6.5632e-01   1.1282 0.2592157    
## electionK-General-2002        -1.9392e-01  5.7696e-01  -0.3361 0.7367945    
## electionK-General-2007         1.3383e+00  4.7332e-01   2.8274 0.0046930 ** 
## electionK-General-2013        -4.5247e-01  8.8896e-01  -0.5090 0.6107637    
## electionK-General-2017        -1.0025e-01  5.1968e-01  -0.1929 0.8470272    
## electionK-General-2022         6.7616e-01  6.2868e-01   1.0755 0.2821373    
## electionOther                 -2.4243e-01  5.4129e-01  -0.4479 0.6542506    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code># Create lists of clustered standard errors

cluster_se7 &lt;- sqrt(diag(vcovCL(model7, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se8 &lt;- sqrt(diag(vcovCL(model8, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se9 &lt;- sqrt(diag(vcovCL(model9, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se10 &lt;- sqrt(diag(vcovCL(model10, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se11 &lt;- sqrt(diag(vcovCL(model11, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se12 &lt;- sqrt(diag(vcovCL(model12, type = &quot;HC&quot;, cluster = ~ district)))

# Format additional lines

additional_lines &lt;- list(
  c(&quot;Election fixed effects&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;),
  c(&quot;Aggregation&quot;, &quot;\\text{Most-inf.}&quot;, &quot;\\text{Most-con.}&quot;, &quot;\\text{Most-inf.}&quot;, &quot;\\text{Most-con.}&quot;, &quot;\\text{Most-inf.}&quot;, &quot;\\text{Most-con.}&quot;)
)

##### Make regression table ####

stargazer(
  model7,
  model8,
  model9,
  model10,
  model11,
  model12,
  digits = 2,
  single.row = FALSE,
  se = list(cluster_se7, cluster_se8, cluster_se9, cluster_se10, cluster_se11, cluster_se12),
  dep.var.labels = c(&quot;Lethal violence&quot;, &quot;Injurious violence&quot;, &quot;Material destruction&quot;),
  column.labels = c(&quot;(7)&quot;, &quot;(8)&quot;, &quot;(9)&quot;, &quot;(10)&quot;, &quot;(11)&quot;, &quot;(12)&quot;),
  model.names = FALSE,
  model.numbers = FALSE,
  covariate.labels = c(
    &quot;Police involvement&quot;,
    &quot;Paramilitary police involvement&quot;,
    &quot;Army involvement&quot;,
    &quot;Agricultural land cover&quot;,
    &quot;Travel time to nearest urban center&quot;
  ),
  omit = c(&quot;election&quot;, &quot;Constant&quot;),
  title = &quot;Accounting for aggregation sensitivity&quot;,
  label = &quot;tab:regression-aggregation&quot;,
  table.placement = &quot;t&quot;,
  add.lines = additional_lines,
  font.size = &quot;scriptsize&quot;,
  notes.append = FALSE,
  star.char = c(&quot;+&quot;, &quot;*&quot;, &quot;**&quot;),
  notes = c(&quot;Robust standard errors in parentheses, clustered by district/county:&quot;, &quot;+ p $&lt;$ 0.1, * p $&lt;$ 0.05, ** p $&lt;$ 0.01&quot;),
  type = &quot;text&quot;
  # type = &quot;latex&quot;,
  # out = &quot;table_d1.tex&quot;
)</code></pre>
<pre><code>## 
## Accounting for aggregation sensitivity
## ===============================================================================================================
##                                                                 Dependent variable:                            
##                                     ---------------------------------------------------------------------------
##                                           Lethal violence           Injurious violence      Material destruction
##                                          (7)           (8)           (9)          (10)        (11)      (12)   
## ---------------------------------------------------------------------------------------------------------------
## Police involvement                      -0.12        -0.59**        -0.03         0.08       -0.49**   -0.64** 
##                                        (0.11)        (0.11)        (0.31)        (0.31)      (0.12)    (0.15)  
##                                                                                                                
## Paramilitary police involvement        0.72**        0.90**         0.56+         0.47        -0.25     -0.09  
##                                        (0.14)        (0.17)        (0.32)        (0.33)      (0.27)    (0.38)  
##                                                                                                                
## Army involvement                        0.56*         0.06          0.22          0.04       -0.52+     -1.57  
##                                        (0.27)        (0.83)        (0.36)        (0.62)      (0.30)    (1.30)  
##                                                                                                                
## Agricultural land cover                -0.001        -0.002         -0.01        -0.01**      0.004     0.01*  
##                                        (0.01)        (0.004)       (0.01)        (0.004)     (0.003)   (0.003) 
##                                                                                                                
## Travel time to nearest urban center    0.002**       0.002**        0.002        0.003*      0.001*    0.002** 
##                                        (0.001)       (0.001)       (0.002)       (0.001)     (0.001)   (0.001) 
##                                                                                                                
## ---------------------------------------------------------------------------------------------------------------
## Election fixed effects                   Yes           Yes           Yes           Yes         Yes       Yes   
## Aggregation                           Most-inf.     Most-con.     Most-inf.     Most-con.   Most-inf. Most-con.
## Observations                            1,916         1,845         1,916         1,845       1,916     1,845  
## Log Likelihood                        -2,615.66     -1,994.45     -3,267.86     -2,506.85    -950.41   -831.49 
## theta                               0.22** (0.01) 0.23** (0.02) 0.21** (0.01) 0.24** (0.01)                    
## Akaike Inf. Crit.                     5,295.32      4,052.89      6,599.72      5,077.71    1,964.81  1,726.98 
## ===============================================================================================================
## Note:                                      Robust standard errors in parentheses, clustered by district/county:
##                                                                              + p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01</code></pre>
<pre class="r"><code>##### Clean working environment ####

rm(
  model7, model8, model9, model10, model11, model12,
  cluster_se7, cluster_se8, cluster_se9, cluster_se10, cluster_se11, cluster_se12,
  additional_lines, maverick_sf_con, maverick_sf_inf
)

#### Using inclusion certainty weights ####

# Create normalized certainty weights

maverick_sf$certain_norm &lt;- maverick_sf$certain / mean(maverick_sf$certain)

summary(maverick_sf$certain_norm)</code></pre>
<pre><code>##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.3898  0.7797  0.7797  1.0000  1.1695  2.3390</code></pre>
<pre class="r"><code>##### Model 13 ####

model13 &lt;- glm.nb(deaths_best ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_sf, link = &quot;log&quot;, weights = certain_norm, control = glm.control(maxit = 200))

# Calculate standard errors clustered by country

coeftest(model13, vcov = vcovCL(model13, cluster = ~ country))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                 Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                  -1.3519e+00  4.5673e-01  -2.9600 0.0030762 ** 
## police_involve               -3.2237e-01  4.4099e-02  -7.3102 2.667e-13 ***
## ppolice_involve               7.3748e-01  5.4860e-02  13.4429 &lt; 2.2e-16 ***
## army_involve                 -2.9193e-01  8.3470e-01  -0.3497 0.7265327    
## agri_gc                      -1.5994e-03  6.7557e-03  -0.2367 0.8128522    
## ttime_mean                    2.9077e-03  7.9662e-04   3.6501 0.0002622 ***
## electionCI-Legislative-2001   6.2679e-01  1.6121e-01   3.8881 0.0001010 ***
## electionCI-Legislative-2011   3.9366e-01  6.2204e-02   6.3285 2.475e-10 ***
## electionCI-Municipal-2013    -3.5935e+01  1.3311e+00 -26.9962 &lt; 2.2e-16 ***
## electionCI-Municipal-2018    -4.6633e-01  3.1886e-02 -14.6249 &lt; 2.2e-16 ***
## electionCI-Presidential-2000  1.5297e+00  1.7067e-01   8.9626 &lt; 2.2e-16 ***
## electionCI-Presidential-2010  2.3749e+00  6.6435e-03 357.4727 &lt; 2.2e-16 ***
## electionCI-Presidential-2020  2.1503e-01  1.5551e-01   1.3828 0.1667326    
## electionK-General-1992        2.0702e+00  6.8069e-02  30.4132 &lt; 2.2e-16 ***
## electionK-General-1997        1.3905e+00  9.2747e-03 149.9197 &lt; 2.2e-16 ***
## electionK-General-2002        7.5875e-01  8.5646e-02   8.8591 &lt; 2.2e-16 ***
## electionK-General-2007        1.4550e+00  2.8378e-02  51.2716 &lt; 2.2e-16 ***
## electionK-General-2013        1.9583e+00  3.0034e-02  65.2035 &lt; 2.2e-16 ***
## electionK-General-2017       -1.2510e-01  3.2825e-02  -3.8111 0.0001384 ***
## electionK-General-2022       -5.4611e-02  1.0223e-01  -0.5342 0.5931919    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 14 ####

model14 &lt;- glm.nb(injuries_best ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_sf, link = &quot;log&quot;, weights = certain_norm)

# Calculate standard errors clustered by country

coeftest(model14, vcov = vcovCL(model14, cluster = ~ country))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                 Estimate  Std. Error   z value  Pr(&gt;|z|)    
## (Intercept)                   1.77470193  0.09874773   17.9721 &lt; 2.2e-16 ***
## police_involve               -0.24687073  0.22627964   -1.0910   0.27527    
## ppolice_involve               0.27559594  0.34223290    0.8053   0.42065    
## army_involve                 -0.23998574  0.49062794   -0.4891   0.62474    
## agri_gc                      -0.01164746  0.00079096  -14.7258 &lt; 2.2e-16 ***
## ttime_mean                    0.00184158  0.00107596    1.7116   0.08698 .  
## electionCI-Legislative-2001  -0.93609224  0.15725679   -5.9526 2.639e-09 ***
## electionCI-Legislative-2011  -2.36516289  0.01634376 -144.7135 &lt; 2.2e-16 ***
## electionCI-Municipal-2013    -1.51138280  0.01614409  -93.6183 &lt; 2.2e-16 ***
## electionCI-Municipal-2018    -2.14415775  0.02143617 -100.0252 &lt; 2.2e-16 ***
## electionCI-Presidential-2000  0.84353328  0.06421103   13.1369 &lt; 2.2e-16 ***
## electionCI-Presidential-2010 -0.91437939  0.02096287  -43.6190 &lt; 2.2e-16 ***
## electionCI-Presidential-2020 -1.33783677  0.04127429  -32.4133 &lt; 2.2e-16 ***
## electionK-General-1992       -0.26264181  0.03155807   -8.3225 &lt; 2.2e-16 ***
## electionK-General-1997       -0.49053270  0.03973240  -12.3459 &lt; 2.2e-16 ***
## electionK-General-2002       -0.37700153  0.04649727   -8.1080 5.144e-16 ***
## electionK-General-2007       -1.88879043  0.03395791  -55.6215 &lt; 2.2e-16 ***
## electionK-General-2013       -0.76968680  0.13457199   -5.7195 1.068e-08 ***
## electionK-General-2017       -1.03348836  0.13752951   -7.5147 5.706e-14 ***
## electionK-General-2022       -1.61003204  0.00865060 -186.1179 &lt; 2.2e-16 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 15 ####

model15 &lt;- glm(damage ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_sf, family = &quot;binomial&quot;, weights = certain_norm)</code></pre>
<pre><code>## Warning in eval(family$initialize): non-integer #successes in a binomial glm!</code></pre>
<pre class="r"><code># Calculate standard errors clustered by country

coeftest(model15, vcov = vcovCL(model15, cluster = ~ country))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                 Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                  -1.72007944  0.12990230 -13.2413 &lt; 2.2e-16 ***
## police_involve               -0.83655844  0.01808197 -46.2648 &lt; 2.2e-16 ***
## ppolice_involve              -0.15498007  0.11028444  -1.4053   0.15994    
## army_involve                 -1.99459222  0.80309759  -2.4836   0.01301 *  
## agri_gc                       0.00628416  0.00020703  30.3535 &lt; 2.2e-16 ***
## ttime_mean                    0.00136911  0.00091482   1.4966   0.13450    
## electionCI-Legislative-2001   0.46108381  0.06850888   6.7303 1.693e-11 ***
## electionCI-Legislative-2011  -0.81028578  0.03859674 -20.9936 &lt; 2.2e-16 ***
## electionCI-Municipal-2013     0.49543554  0.02689037  18.4243 &lt; 2.2e-16 ***
## electionCI-Municipal-2018     0.77386356  0.02110957  36.6594 &lt; 2.2e-16 ***
## electionCI-Presidential-2000 -0.86718477  0.07422398 -11.6834 &lt; 2.2e-16 ***
## electionCI-Presidential-2010 -0.53964322  0.03176352 -16.9894 &lt; 2.2e-16 ***
## electionCI-Presidential-2020  1.57029242  0.04741390  33.1188 &lt; 2.2e-16 ***
## electionK-General-1992        0.08977912  0.07494804   1.1979   0.23096    
## electionK-General-1997        0.42472550  0.08881544   4.7821 1.735e-06 ***
## electionK-General-2002       -0.83675018  0.08935532  -9.3643 &lt; 2.2e-16 ***
## electionK-General-2007        1.07824508  0.03212576  33.5633 &lt; 2.2e-16 ***
## electionK-General-2013       -0.20406761  0.17895259  -1.1403   0.25414    
## electionK-General-2017       -0.41942711  0.02689323 -15.5960 &lt; 2.2e-16 ***
## electionK-General-2022        0.59456455  0.05672208  10.4821 &lt; 2.2e-16 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code># Create lists of clustered standard errors

cluster_se13 &lt;- sqrt(diag(vcovCL(model13, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se14 &lt;- sqrt(diag(vcovCL(model14, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se15 &lt;- sqrt(diag(vcovCL(model15, type = &quot;HC&quot;, cluster = ~ district)))

# Format additional lines

additional_lines &lt;- list(
  c(&quot;Election fixed effects&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;),
  c(&quot;Inclusion certainty weights&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;),
  c(&quot;Aggregation&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;)
)

##### Make regression table ####

stargazer(
  model13,
  model14,
  model15,
  digits = 2,
  single.row = FALSE,
  se = list(cluster_se13, cluster_se14, cluster_se15),
  dep.var.labels = c(&quot;Lethal violence&quot;, &quot;Injurious violence&quot;, &quot;Material destruction&quot;),
  column.labels = c(&quot;(13)&quot;, &quot;(14)&quot;, &quot;(15)&quot;),
  model.names = FALSE,
  model.numbers = FALSE,
  covariate.labels = c(
    &quot;Police involvement&quot;,
    &quot;Paramilitary police involvement&quot;,
    &quot;Army involvement&quot;,
    &quot;Agricultural land cover&quot;,
    &quot;Travel time to nearest urban center&quot;
  ),
  omit = c(&quot;election&quot;, &quot;Constant&quot;),
  title = &quot;Using inclusion certainty weights&quot;,
  label = &quot;tab:regression-certainty&quot;,
  table.placement = &quot;t&quot;,
  add.lines = additional_lines,
  font.size = &quot;scriptsize&quot;,
  notes.append = FALSE,
  star.char = c(&quot;+&quot;, &quot;*&quot;, &quot;**&quot;),
  notes = c(&quot;All models include inclusion certainty weights.&quot;, &quot;Robust standard errors in parentheses, clustered by district/county:&quot;, &quot;+ p $&lt;$ 0.1, * p $&lt;$ 0.05, ** p $&lt;$ 0.01&quot;),
  type = &quot;text&quot;
  # type = &quot;latex&quot;,
  # out = &quot;table_d2.tex&quot;
)</code></pre>
<pre><code>## 
## Using inclusion certainty weights
## ==========================================================================================================
##                                                              Dependent variable:                          
##                                     ----------------------------------------------------------------------
##                                         Lethal violence       Injurious violence     Material destruction 
##                                              (13)                    (14)                    (15)         
## ----------------------------------------------------------------------------------------------------------
## Police involvement                          -0.32**                  -0.25                 -0.84**        
##                                             (0.10)                  (0.29)                  (0.14)        
##                                                                                                           
## Paramilitary police involvement             0.74**                   0.28                   -0.15         
##                                             (0.15)                  (0.38)                  (0.36)        
##                                                                                                           
## Army involvement                             -0.29                   -0.24                  -1.99         
##                                             (0.78)                  (0.48)                  (1.27)        
##                                                                                                           
## Agricultural land cover                     -0.002                  -0.01**                 0.01*         
##                                             (0.004)                 (0.004)                (0.003)        
##                                                                                                           
## Travel time to nearest urban center         0.003**                  0.002                  0.001*        
##                                             (0.001)                 (0.001)                (0.001)        
##                                                                                                           
## ----------------------------------------------------------------------------------------------------------
## Election fixed effects                        Yes                     Yes                    Yes          
## Inclusion certainty weights                   Yes                     Yes                    Yes          
## Aggregation                                Most-rep.               Most-rep.              Most-rep.       
## Observations                                 1,780                   1,780                  1,780         
## Log Likelihood                             -2,207.69               -2,367.08               -923.03        
## theta                                    0.24** (0.01)           0.20** (0.01)                            
## Akaike Inf. Crit.                          4,455.38                4,774.16                1,886.07       
## ==========================================================================================================
## Note:                                                      All models include inclusion certainty weights.
##                                       Robust standard errors in parentheses, clustered by district/county:
##                                                                         + p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01</code></pre>
<pre class="r"><code>##### Clean working environment ####

rm(
  model13, model14, model15,
  cluster_se13, cluster_se14, cluster_se15, 
  additional_lines
)

#### Omitting events with missing actor information ####

# Exclude events that include Unknown or Indeterminate actors in actor*_type

maverick_uncertain &lt;- maverick_sf %&gt;% 
  mutate(
    uncertain = case_when(
      actor1_type %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor1_bystander != 1 ~ 1,
      actor2_type %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor2_bystander != 1 ~ 1,
      actor3_type %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor3_bystander != 1 ~ 1,
      actor4_type %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor4_bystander != 1 ~ 1,
      actor5_type %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor5_bystander != 1 ~ 1,
      actor6_type %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    )
  ) %&gt;% 
  filter(uncertain == 0)

# Check that the sub-setting worked

maverick_sf %&gt;% 
  mutate(
    uncertain = case_when(
      actor1_type %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor1_bystander != 1 ~ 1,
      actor2_type %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor2_bystander != 1 ~ 1,
      actor3_type %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor3_bystander != 1 ~ 1,
      actor4_type %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor4_bystander != 1 ~ 1,
      actor5_type %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor5_bystander != 1 ~ 1,
      actor6_type %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    )
  ) %&gt;% 
  filter(uncertain == 1) %&gt;% 
  select(uncertain, actor1_type, actor2_type, actor3_type, actor4_type, actor5_type, actor6_type)</code></pre>
<pre><code>## Simple feature collection with 361 features and 7 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -8.21224 ymin: -4.2929 xmax: 41.85688 ymax: 10.00244
## Geodetic CRS:  WGS 84
## # A tibble: 361 × 8
##    uncertain actor1_type       actor2_type   actor3_type actor4_type actor5_type
##        &lt;dbl&gt; &lt;chr&gt;             &lt;chr&gt;         &lt;chr&gt;       &lt;chr&gt;       &lt;chr&gt;      
##  1         1 &quot;Political actor&quot; &quot;Indetermina… &lt;NA&gt;        &lt;NA&gt;        &lt;NA&gt;       
##  2         1 &quot;Security forces&quot; &quot;Indetermina… &lt;NA&gt;        &lt;NA&gt;        &lt;NA&gt;       
##  3         1 &quot;Political actor&quot; &quot;Political a… Indetermin… &lt;NA&gt;        &lt;NA&gt;       
##  4         1 &quot;&quot;                 &lt;NA&gt;         &lt;NA&gt;        &lt;NA&gt;        &lt;NA&gt;       
##  5         1 &quot;&quot;                 &lt;NA&gt;         &lt;NA&gt;        &lt;NA&gt;        &lt;NA&gt;       
##  6         1 &quot;&quot;                 &lt;NA&gt;         &lt;NA&gt;        &lt;NA&gt;        &lt;NA&gt;       
##  7         1 &quot;&quot;                 &lt;NA&gt;         &lt;NA&gt;        &lt;NA&gt;        &lt;NA&gt;       
##  8         1 &quot;&quot;                 &lt;NA&gt;         &lt;NA&gt;        &lt;NA&gt;        &lt;NA&gt;       
##  9         1  &lt;NA&gt;             &quot;&quot;            &lt;NA&gt;        &lt;NA&gt;        &lt;NA&gt;       
## 10         1 &quot;&quot;                 &lt;NA&gt;         &lt;NA&gt;        &lt;NA&gt;        &lt;NA&gt;       
## # ℹ 351 more rows
## # ℹ 2 more variables: actor6_type &lt;chr&gt;, geometry &lt;POINT [°]&gt;</code></pre>
<pre class="r"><code># Exclude events that include Unknown or Indeterminate actors in actor*_subtype

maverick_uncertain2 &lt;- maverick_sf %&gt;% 
  mutate(
    uncertain = case_when(
      actor1_subtype %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor1_bystander != 1 ~ 1,
      actor2_subtype %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor2_bystander != 1 ~ 1,
      actor3_subtype %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor3_bystander != 1 ~ 1,
      actor4_subtype %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor4_bystander != 1 ~ 1,
      actor5_subtype %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor5_bystander != 1 ~ 1,
      actor6_subtype %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    )
  ) %&gt;% 
  filter(uncertain == 0)

# Check that the sub-setting worked

maverick_sf %&gt;% 
  mutate(
    uncertain = case_when(
      actor1_subtype %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor1_bystander != 1 ~ 1,
      actor2_subtype %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor2_bystander != 1 ~ 1,
      actor3_subtype %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor3_bystander != 1 ~ 1,
      actor4_subtype %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor4_bystander != 1 ~ 1,
      actor5_subtype %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor5_bystander != 1 ~ 1,
      actor6_subtype %in% c(&quot;&quot;, &quot;Indeterminate&quot;) &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    )
  ) %&gt;% 
  filter(uncertain == 1) %&gt;% 
  select(uncertain, actor1_subtype, actor2_subtype, actor3_subtype, actor4_subtype, actor5_subtype, actor6_subtype)</code></pre>
<pre><code>## Simple feature collection with 505 features and 7 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -8.41091 ymin: -4.2929 xmax: 41.85688 ymax: 10.00244
## Geodetic CRS:  WGS 84
## # A tibble: 505 × 8
##    uncertain actor1_subtype         actor2_subtype actor3_subtype actor4_subtype
##        &lt;dbl&gt; &lt;chr&gt;                  &lt;chr&gt;          &lt;chr&gt;          &lt;chr&gt;         
##  1         1 &quot;&quot;                     &quot;Citizens&quot;     &lt;NA&gt;           &lt;NA&gt;          
##  2         1 &quot;&quot;                      &lt;NA&gt;          &lt;NA&gt;           &lt;NA&gt;          
##  3         1 &quot;&quot;                     &quot;Indeterminat… &lt;NA&gt;           &lt;NA&gt;          
##  4         1 &quot;Security forces: Pol… &quot;&quot;             &lt;NA&gt;           &lt;NA&gt;          
##  5         1 &quot;&quot;                     &quot;Indeterminat… &lt;NA&gt;           &lt;NA&gt;          
##  6         1 &quot;&quot;                      &lt;NA&gt;          &lt;NA&gt;           &lt;NA&gt;          
##  7         1 &quot;&quot;                      &lt;NA&gt;          &lt;NA&gt;           &lt;NA&gt;          
##  8         1 &quot;Political actor: Par… &quot;Political ac… Indeterminate  &lt;NA&gt;          
##  9         1 &quot;&quot;                     &quot;Political ac… &lt;NA&gt;           &lt;NA&gt;          
## 10         1 &quot;&quot;                      &lt;NA&gt;          &lt;NA&gt;           &lt;NA&gt;          
## # ℹ 495 more rows
## # ℹ 3 more variables: actor5_subtype &lt;chr&gt;, actor6_subtype &lt;chr&gt;,
## #   geometry &lt;POINT [°]&gt;</code></pre>
<pre class="r"><code>##### Model 16 ####

model1 &lt;- glm.nb(deaths_best ~ sf_involve + agri_gc + ttime_mean + election, data = maverick_uncertain, link = &quot;log&quot;)

# Calculate standard errors clustered by district

coeftest(model1, vcov = vcovCL(model1, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                 Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                  -1.2675e+00  5.7304e-01  -2.2119 0.0269741 *  
## sf_involve                   -1.4874e-01  1.1825e-01  -1.2578 0.2084678    
## agri_gc                      -4.8263e-03  4.5235e-03  -1.0669 0.2860035    
## ttime_mean                    3.9195e-03  7.6443e-04   5.1273 2.939e-07 ***
## electionCI-Legislative-2001   1.3456e+00  5.6765e-01   2.3705 0.0177658 *  
## electionCI-Legislative-2011  -3.2182e-01  8.7538e-01  -0.3676 0.7131473    
## electionCI-Municipal-2013    -3.4206e+01  5.5700e-01 -61.4101 &lt; 2.2e-16 ***
## electionCI-Municipal-2018    -3.2581e-01  5.9130e-01  -0.5510 0.5816327    
## electionCI-Presidential-2000  2.1559e+00  5.5538e-01   3.8819 0.0001037 ***
## electionCI-Presidential-2010  2.2319e+00  4.1376e-01   5.3943 6.880e-08 ***
## electionCI-Presidential-2020  2.7571e-01  4.1120e-01   0.6705 0.5025388    
## electionK-General-1992        1.8309e+00  7.2989e-01   2.5084 0.0121282 *  
## electionK-General-1997        8.4195e-01  7.7255e-01   1.0898 0.2757922    
## electionK-General-2002        7.0984e-01  8.2280e-01   0.8627 0.3882920    
## electionK-General-2007        1.1364e+00  5.0020e-01   2.2719 0.0230904 *  
## electionK-General-2013        1.4572e+00  5.1472e-01   2.8310 0.0046405 ** 
## electionK-General-2017       -4.0858e-01  6.3154e-01  -0.6470 0.5176626    
## electionK-General-2022       -9.9037e-03  5.5694e-01  -0.0178 0.9858125    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 17 ####

model2 &lt;- glm.nb(deaths_best ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_uncertain2, link = &quot;log&quot;)

# Calculate standard errors clustered by district

coeftest(model2, vcov = vcovCL(model2, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                 Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                  -1.7150e+00  4.1060e-01  -4.1767 2.957e-05 ***
## police_involve               -2.2341e-01  1.0956e-01  -2.0392 0.0414283 *  
## ppolice_involve               7.0868e-01  1.2397e-01   5.7167 1.086e-08 ***
## army_involve                 -1.0693e+00  2.3716e-01  -4.5090 6.514e-06 ***
## agri_gc                      -1.0818e-02  3.6679e-03  -2.9495 0.0031831 ** 
## ttime_mean                    3.0653e-03  9.8412e-04   3.1148 0.0018409 ** 
## electionCI-Legislative-2001   1.4430e+00  3.9908e-01   3.6159 0.0002993 ***
## electionCI-Legislative-2011   5.7224e-01  9.2087e-01   0.6214 0.5343299    
## electionCI-Municipal-2013    -3.1485e+01  9.5999e-01 -32.7972 &lt; 2.2e-16 ***
## electionCI-Municipal-2018     1.1260e+00  5.1485e-01   2.1870 0.0287406 *  
## electionCI-Presidential-2000  2.2705e+00  4.2522e-01   5.3397 9.311e-08 ***
## electionCI-Presidential-2010  2.6204e+00  3.9734e-01   6.5950 4.253e-11 ***
## electionCI-Presidential-2020  1.4661e+00  4.3342e-01   3.3827 0.0007178 ***
## electionK-General-1992        3.0198e+00  6.6323e-01   4.5531 5.285e-06 ***
## electionK-General-1997        1.0118e+00  6.5380e-01   1.5475 0.1217319    
## electionK-General-2002        1.4798e+00  7.6830e-01   1.9260 0.0541031 .  
## electionK-General-2007        1.9580e+00  4.3171e-01   4.5355 5.745e-06 ***
## electionK-General-2013        2.4172e+00  4.8590e-01   4.9746 6.538e-07 ***
## electionK-General-2017        3.5082e-01  5.3539e-01   0.6553 0.5122945    
## electionK-General-2022        1.0423e+00  4.7793e-01   2.1808 0.0291946 *  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 18 ####

model3 &lt;- glm.nb(injuries_best ~ sf_involve + agri_gc + ttime_mean + election, data = maverick_uncertain, link = &quot;log&quot;)

# Calculate standard errors clustered by district

coeftest(model3, vcov = vcovCL(model3, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                 Estimate  Std. Error  z value Pr(&gt;|z|)    
## (Intercept)                   1.5831e+00  8.6536e-01   1.8294  0.06735 .  
## sf_involve                    2.0156e-02  1.7638e-01   0.1143  0.90902    
## agri_gc                      -6.7588e-03  3.0173e-03  -2.2400  0.02509 *  
## ttime_mean                   -4.9212e-04  7.2340e-04  -0.6803  0.49632    
## electionCI-Legislative-2001  -3.7461e-01  8.9152e-01  -0.4202  0.67435    
## electionCI-Legislative-2011  -2.9494e+01  7.5229e-01 -39.2056  &lt; 2e-16 ***
## electionCI-Municipal-2013    -1.0191e+00  8.5855e-01  -1.1870  0.23522    
## electionCI-Municipal-2018    -2.3879e+00  9.7324e-01  -2.4535  0.01415 *  
## electionCI-Presidential-2000  1.1257e+00  8.8298e-01   1.2749  0.20235    
## electionCI-Presidential-2010 -6.9226e-01  8.2911e-01  -0.8349  0.40375    
## electionCI-Presidential-2020 -1.0936e+00  8.6894e-01  -1.2585  0.20821    
## electionK-General-1992        3.4169e-01  1.0246e+00   0.3335  0.73878    
## electionK-General-1997        2.3713e-01  8.9009e-01   0.2664  0.78992    
## electionK-General-2002        1.5005e-01  8.4334e-01   0.1779  0.85878    
## electionK-General-2007       -1.4584e+00  8.1803e-01  -1.7828  0.07462 .  
## electionK-General-2013       -9.2982e-02  1.0632e+00  -0.0875  0.93031    
## electionK-General-2017       -1.1304e+00  8.0396e-01  -1.4061  0.15970    
## electionK-General-2022       -1.1487e+00  9.0437e-01  -1.2702  0.20401    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 19 ####

model4 &lt;- glm.nb(injuries_best ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_uncertain2, link = &quot;log&quot;)

# Calculate standard errors clustered by district

coeftest(model4, vcov = vcovCL(model4, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                 Estimate  Std. Error  z value Pr(&gt;|z|)    
## (Intercept)                   2.0859e+00  9.3459e-01   2.2319  0.02562 *  
## police_involve               -4.0074e-03  1.9781e-01  -0.0203  0.98384    
## ppolice_involve               1.8741e-01  3.8376e-01   0.4884  0.62530    
## army_involve                 -9.2756e-02  5.1833e-01  -0.1790  0.85798    
## agri_gc                      -5.9886e-03  2.8598e-03  -2.0941  0.03625 *  
## ttime_mean                   -5.6965e-04  7.3700e-04  -0.7729  0.43957    
## electionCI-Legislative-2001  -9.5636e-01  1.0880e+00  -0.8790  0.37942    
## electionCI-Legislative-2011  -3.4936e+01  8.0717e-01 -43.2826  &lt; 2e-16 ***
## electionCI-Municipal-2013    -3.5101e+01  8.3719e-01 -41.9279  &lt; 2e-16 ***
## electionCI-Municipal-2018    -2.1593e+00  1.2575e+00  -1.7172  0.08594 .  
## electionCI-Presidential-2000  5.5361e-01  1.0424e+00   0.5311  0.59536    
## electionCI-Presidential-2010 -1.1555e+00  8.8946e-01  -1.2991  0.19390    
## electionCI-Presidential-2020 -1.4430e+00  9.7446e-01  -1.4808  0.13867    
## electionK-General-1992       -2.7560e-01  1.1252e+00  -0.2449  0.80651    
## electionK-General-1997       -6.4692e-01  9.9905e-01  -0.6475  0.51729    
## electionK-General-2002       -3.6589e-01  9.2924e-01  -0.3937  0.69377    
## electionK-General-2007       -1.9289e+00  9.2021e-01  -2.0961  0.03607 *  
## electionK-General-2013       -6.1136e-01  1.1373e+00  -0.5376  0.59089    
## electionK-General-2017       -1.6457e+00  9.1508e-01  -1.7984  0.07211 .  
## electionK-General-2022       -1.6162e+00  1.0455e+00  -1.5459  0.12212    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 20 ####

model5 &lt;- glm(damage ~ sf_involve + agri_gc + ttime_mean + election, data = maverick_uncertain, family = &quot;binomial&quot;)

# Calculate standard errors clustered by district

coeftest(model5, vcov = vcovCL(model5, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                 Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                  -1.2871e+00  4.3278e-01  -2.9740  0.002939 ** 
## sf_involve                   -8.1239e-01  1.6135e-01  -5.0350 4.779e-07 ***
## agri_gc                       4.4208e-03  3.6353e-03   1.2161  0.223953    
## ttime_mean                    1.1605e-03  7.5661e-04   1.5338  0.125079    
## electionCI-Legislative-2001  -3.6987e-01  6.4586e-01  -0.5727  0.566864    
## electionCI-Legislative-2011  -1.3455e+01  7.4632e-01 -18.0290 &lt; 2.2e-16 ***
## electionCI-Municipal-2013     1.0580e-02  5.7291e-01   0.0185  0.985266    
## electionCI-Municipal-2018     9.2573e-01  6.3357e-01   1.4611  0.143975    
## electionCI-Presidential-2000 -9.0380e-01  3.7034e-01  -2.4404  0.014669 *  
## electionCI-Presidential-2010 -9.1629e-01  3.8309e-01  -2.3918  0.016764 *  
## electionCI-Presidential-2020  1.0574e+00  4.8885e-01   2.1629  0.030546 *  
## electionK-General-1992       -2.5882e-01  6.4527e-01  -0.4011  0.688344    
## electionK-General-1997        2.3239e-03  6.1460e-01   0.0038  0.996983    
## electionK-General-2002       -1.1336e+00  5.6485e-01  -2.0068  0.044766 *  
## electionK-General-2007        8.7485e-01  3.9472e-01   2.2164  0.026664 *  
## electionK-General-2013       -4.2310e-01  6.3994e-01  -0.6612  0.508515    
## electionK-General-2017       -7.5969e-01  4.5895e-01  -1.6553  0.097867 .  
## electionK-General-2022        7.3791e-02  6.7019e-01   0.1101  0.912326    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 21 ####

model6 &lt;- glm(damage ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_uncertain2, family = &quot;binomial&quot;)</code></pre>
<pre><code>## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred</code></pre>
<pre class="r"><code># Calculate standard errors clustered by district

coeftest(model6, vcov = vcovCL(model6, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                 Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                   -1.0316941   0.6173682  -1.6711   0.09470 .  
## police_involve                -0.9741101   0.1631522  -5.9706 2.364e-09 ***
## ppolice_involve               -0.2277215   0.3404364  -0.6689   0.50355    
## army_involve                 -15.3181568   0.7409590 -20.6734 &lt; 2.2e-16 ***
## agri_gc                        0.0057824   0.0034119   1.6948   0.09012 .  
## ttime_mean                     0.0011877   0.0008409   1.4124   0.15784    
## electionCI-Legislative-2001   -0.9063534   0.8581532  -1.0562   0.29089    
## electionCI-Legislative-2011  -15.5424277   0.8684213 -17.8973 &lt; 2.2e-16 ***
## electionCI-Municipal-2013      0.6896847   0.5746959   1.2001   0.23011    
## electionCI-Municipal-2018      0.4267797   1.4118454   0.3023   0.76243    
## electionCI-Presidential-2000  -1.3971984   0.5564736  -2.5108   0.01205 *  
## electionCI-Presidential-2010  -1.3474795   0.6072130  -2.2191   0.02648 *  
## electionCI-Presidential-2020   0.5652651   0.8057332   0.7016   0.48296    
## electionK-General-1992        -0.5711458   0.7721500  -0.7397   0.45949    
## electionK-General-1997        -0.1339598   0.7748719  -0.1729   0.86275    
## electionK-General-2002        -1.4489889   0.7099606  -2.0409   0.04126 *  
## electionK-General-2007         0.6561055   0.5768578   1.1374   0.25538    
## electionK-General-2013        -0.6685880   0.7860024  -0.8506   0.39498    
## electionK-General-2017        -0.9998583   0.6005988  -1.6648   0.09596 .  
## electionK-General-2022        -0.5851721   0.8248614  -0.7094   0.47806    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code># Create lists of clustered standard errors

cluster_se1 &lt;- sqrt(diag(vcovCL(model1, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se2 &lt;- sqrt(diag(vcovCL(model2, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se3 &lt;- sqrt(diag(vcovCL(model3, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se4 &lt;- sqrt(diag(vcovCL(model4, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se5 &lt;- sqrt(diag(vcovCL(model5, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se6 &lt;- sqrt(diag(vcovCL(model6, type = &quot;HC&quot;, cluster = ~ district)))

# Format additional lines

additional_lines &lt;- list(
  c(&quot;Election fixed effects&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;),
  c(&quot;Aggregation&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;)
)

##### Make regression table ####

stargazer(
  model1,
  model2,
  model3,
  model4,
  model5,
  model6,
  digits = 2,
  single.row = FALSE,
  se = list(cluster_se1, cluster_se2, cluster_se3, cluster_se4, cluster_se5, cluster_se6),
  dep.var.labels = c(&quot;Lethal violence&quot;, &quot;Injurious violence&quot;, &quot;Material destruction&quot;),
  column.labels = c(&quot;(16)&quot;, &quot;(17)&quot;, &quot;(18)&quot;, &quot;(19)&quot;, &quot;(20)&quot;, &quot;(21)&quot;),
  model.names = FALSE,
  model.numbers = FALSE,
  covariate.labels = c(
    &quot;Security force involvement&quot;,
    &quot;Police involvement&quot;,
    &quot;Paramilitary police involvement&quot;,
    &quot;Army involvement&quot;,
    &quot;Agricultural land cover&quot;,
    &quot;Travel time to nearest urban center&quot;
  ),
  omit = c(&quot;Constant&quot;, &quot;election&quot;),
  title = &quot;Omitting events with missing actor information&quot;,
  label = &quot;tab:regression-actors&quot;,
  table.placement = &quot;t&quot;,
  add.lines = additional_lines,
  font.size = &quot;scriptsize&quot;,
  notes.append = FALSE,
  star.char = c(&quot;+&quot;, &quot;*&quot;, &quot;**&quot;),
  notes = c(
    &quot;Models 16, 18, and 20 omit events that include at least one unknown actor type.&quot;,
    &quot;Models 17, 19, and 21 omit events that include at least one unknown actor subtype.&quot;,
    &quot;Robust standard errors in parentheses, clustered by district/county:&quot;, &quot;+ p $&lt;$ 0.1, * p $&lt;$ 0.05, ** p $&lt;$ 0.01&quot;
  ),
  type = &quot;text&quot;
  # type = &quot;latex&quot;,
  # out = &quot;table_d3.tex&quot;
)</code></pre>
<pre><code>## 
## Omitting events with missing actor information
## ===========================================================================================================================
##                                                                       Dependent variable:                                  
##                                     ---------------------------------------------------------------------------------------
##                                              Lethal violence                Injurious violence        Material destruction 
##                                           (16)             (17)             (18)            (19)         (20)       (21)   
## ---------------------------------------------------------------------------------------------------------------------------
## Security force involvement               -0.15                              0.02                       -0.81**             
##                                          (0.12)                            (0.18)                       (0.16)             
##                                                                                                                            
## Police involvement                                        -0.22*                           -0.004                 -0.97**  
##                                                           (0.11)                           (0.20)                  (0.16)  
##                                                                                                                            
## Paramilitary police involvement                           0.71**                            0.19                   -0.23   
##                                                           (0.12)                           (0.38)                  (0.34)  
##                                                                                                                            
## Army involvement                                         -1.07**                           -0.09                  -15.32** 
##                                                           (0.24)                           (0.52)                  (0.74)  
##                                                                                                                            
## Agricultural land cover                  -0.005          -0.01**           -0.01*          -0.01*       0.004      0.01+   
##                                         (0.005)          (0.004)          (0.003)         (0.003)      (0.004)    (0.003)  
##                                                                                                                            
## Travel time to nearest urban center     0.004**          0.003**          -0.0005          -0.001       0.001      0.001   
##                                         (0.001)          (0.001)          (0.001)         (0.001)      (0.001)    (0.001)  
##                                                                                                                            
## ---------------------------------------------------------------------------------------------------------------------------
## Election fixed effects                    Yes              Yes              Yes             Yes          Yes        Yes    
## Aggregation                            Most-rep.        Most-rep.        Most-rep.       Most-rep.    Most-rep.  Most-rep. 
## Observations                             1,419            1,275            1,419           1,275        1,419      1,275   
## Log Likelihood                         -1,633.53        -1,341.69        -2,027.03       -1,863.63     -615.37    -545.18  
## theta                                0.24** (0.02)    0.26** (0.02)    0.29** (0.02)   0.32** (0.02)                       
## Akaike Inf. Crit.                       3,303.06         2,723.37         4,090.05        3,767.25     1,266.74   1,130.36 
## ===========================================================================================================================
## Note:                                       Models 16, 18, and 20 omit events that include at least one unknown actor type.
##                                          Models 17, 19, and 21 omit events that include at least one unknown actor subtype.
##                                                        Robust standard errors in parentheses, clustered by district/county:
##                                                                                          + p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01</code></pre>
<pre class="r"><code>##### Test that the difference in coefficients is statistically significant ####

vcov_cluster &lt;- vcovCL(model2, cluster = ~district)

# Test if police_involve = ppolice_involve

linearHypothesis(model2, &quot;police_involve = ppolice_involve&quot;, vcov. = vcov_cluster)</code></pre>
<pre><code>## 
## Linear hypothesis test:
## police_involve - ppolice_involve = 0
## 
## Model 1: restricted model
## Model 2: deaths_best ~ police_involve + ppolice_involve + army_involve + 
##     agri_gc + ttime_mean + election
## 
## Note: Coefficient covariance matrix supplied.
## 
##   Res.Df Df  Chisq Pr(&gt;Chisq)    
## 1   1256                         
## 2   1255  1 30.304  3.693e-08 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code># Test if police_involve = army_involve

linearHypothesis(model2, &quot;police_involve = army_involve&quot;, vcov. = vcov_cluster)</code></pre>
<pre><code>## 
## Linear hypothesis test:
## police_involve - army_involve = 0
## 
## Model 1: restricted model
## Model 2: deaths_best ~ police_involve + ppolice_involve + army_involve + 
##     agri_gc + ttime_mean + election
## 
## Note: Coefficient covariance matrix supplied.
## 
##   Res.Df Df  Chisq Pr(&gt;Chisq)    
## 1   1256                         
## 2   1255  1 12.426  0.0004235 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Clean working environment ####

rm(
  model1, model2, model3, model4, model5, model6,
  cluster_se1, cluster_se2, cluster_se3, cluster_se4, cluster_se5, cluster_se6, vcov_cluster,
  additional_lines, maverick_uncertain, maverick_uncertain2
)

#### Including only events sourced from news sources ####

# Subset event report data to only include sources from Factiva

maverick_media &lt;- maverick %&gt;% 
  filter(
    source_type %in% c(
      &quot;International news agency&quot;,
      &quot;International news media&quot;,
      &quot;National news media&quot;
    )
  )

maverick_reports &lt;- maverick %&gt;% 
  filter(
    source_type %in% c(
      &quot;Human rights commission&quot;,
      &quot;International civil society organization&quot;,
      &quot;International organization&quot;,
      &quot;National civil society organization&quot;,
      &quot;National government agency&quot;,
      &quot;Other&quot;,
      &quot;Special commission&quot;
    )
  )

# Count number of reports in the filtered dataset

writeLines(paste0(format(nrow(maverick_media), big.mark = &quot;,&quot;), &quot;%&quot;), &quot;/Users/sebastian/Library/CloudStorage/Dropbox/Appar/Overleaf/Introducing the MAVERICK Dataset/number_of_event_reports_media.tex&quot;)

# Re-specify the most-representative aggregation model

revised_aggregation &lt;- function(data) {
  aggregateData(
    data = data,
    group_var = &quot;event_id&quot;,
    find_mode = c(
      &quot;country&quot;, &quot;election&quot;, &quot;city&quot;, &quot;location&quot;,
      &quot;actor1&quot;, &quot;actor1_id&quot;, &quot;actor1_type&quot;, &quot;actor1_subtype&quot;, &quot;actor1_party&quot;, &quot;actor1_violence&quot;,
      &quot;actor2&quot;, &quot;actor2_id&quot;, &quot;actor2_type&quot;, &quot;actor2_subtype&quot;, &quot;actor2_party&quot;, &quot;actor2_violence&quot;,
      &quot;actor3&quot;, &quot;actor3_id&quot;, &quot;actor3_type&quot;, &quot;actor3_subtype&quot;, &quot;actor3_party&quot;, &quot;actor3_violence&quot;,
      &quot;actor4&quot;, &quot;actor4_id&quot;, &quot;actor4_type&quot;, &quot;actor4_subtype&quot;, &quot;actor4_party&quot;, &quot;actor4_violence&quot;,
      &quot;actor5&quot;, &quot;actor5_id&quot;, &quot;actor5_type&quot;, &quot;actor5_subtype&quot;, &quot;actor5_party&quot;, &quot;actor5_violence&quot;,
      &quot;actor6&quot;, &quot;actor6_id&quot;, &quot;actor6_type&quot;, &quot;actor6_subtype&quot;, &quot;actor6_party&quot;, &quot;actor6_violence&quot;,
      &quot;event_context&quot;, &quot;target&quot;
    ),
    find_mode_date = c(&quot;date_start&quot;, &quot;date_end&quot;),
    find_mode_numeric = c(
      &quot;latitude&quot;, &quot;longitude&quot;, &quot;geo_precision&quot;,
      &quot;actor1_victim&quot;, &quot;actor2_victim&quot;, &quot;actor3_victim&quot;, &quot;actor4_victim&quot;, &quot;actor5_victim&quot;, &quot;actor6_victim&quot;,
      &quot;deaths_best&quot;, &quot;deaths_low&quot;, &quot;deaths_high&quot;, &quot;injuries_best&quot;, &quot;injuries_low&quot;, &quot;injuries_high&quot;
    ),
    find_mode_bin = c(&quot;displacement&quot;, &quot;damage&quot;),
    summarize_vars = c(
      &quot;actor1_initiator&quot;, &quot;actor1_perpetrator&quot;, &quot;actor1_intervener&quot;, &quot;actor1_bystander&quot;,
      &quot;actor2_initiator&quot;, &quot;actor2_perpetrator&quot;, &quot;actor2_intervener&quot;, &quot;actor2_bystander&quot;,
      &quot;actor3_initiator&quot;, &quot;actor3_perpetrator&quot;, &quot;actor3_intervener&quot;, &quot;actor3_bystander&quot;,
      &quot;actor4_initiator&quot;, &quot;actor4_perpetrator&quot;, &quot;actor4_intervener&quot;, &quot;actor4_bystander&quot;,
      &quot;actor5_initiator&quot;, &quot;actor5_perpetrator&quot;, &quot;actor5_intervener&quot;, &quot;actor5_bystander&quot;,
      &quot;actor6_initiator&quot;, &quot;actor6_perpetrator&quot;, &quot;actor6_intervener&quot;, &quot;actor6_bystander&quot;
    ),
    combine_strings = &quot;source&quot;,
    find_max = c(&quot;certain1&quot;, &quot;certain2&quot;, &quot;certain3&quot;, &quot;certain4&quot;, &quot;certain5&quot;, &quot;certain6&quot;),
    tie_break = &quot;source_classification&quot;,
    second_tie_break = &quot;certain&quot;,
    aggregation_name = &quot;Representative&quot;
  ) %&gt;%
    mutate(certain = certain1 + certain2 + certain3 + certain4 + certain5 + certain6) %&gt;%
    rowwise() %&gt;%
    mutate(
      across(
        matches(&quot;^actor[1-6]_initiator$&quot;),
        ~ {
          corresponding_actor &lt;- get(gsub(&quot;_initiator&quot;, &quot;&quot;, cur_column()))
          if (is.na(corresponding_actor)) {
            NA_integer_
          } else {
            max_value &lt;- max(c_across(matches(&quot;^actor[1-6]_initiator$&quot;)), na.rm = TRUE)
            num_max &lt;- sum(c_across(matches(&quot;^actor[1-6]_initiator$&quot;)) == max_value, na.rm = TRUE)
            ifelse(. == max_value &amp; max_value &gt; 0 &amp; num_max == 1, 1, 0)
          }
        }
      ),
      across(
        matches(&quot;^actor[1-6]_perpetrator$&quot;),
        ~ {
          corresponding_actor &lt;- get(gsub(&quot;_perpetrator&quot;, &quot;&quot;, cur_column()))
          if (is.na(corresponding_actor)) {
            NA_integer_
          } else {
            ifelse(. &gt; 0 &amp; get(gsub(&quot;_perpetrator&quot;, &quot;_bystander&quot;, cur_column())) &lt; 1, 1, 0)
          }
        }
      ),
      across(
        matches(&quot;^actor[1-6]_intervener$&quot;),
        ~ {
          corresponding_actor &lt;- get(gsub(&quot;_intervener&quot;, &quot;&quot;, cur_column()))
          if (is.na(corresponding_actor)) {
            NA_integer_
          } else {
            ifelse(. &gt; 0 &amp; get(gsub(&quot;_intervener&quot;, &quot;_initiator&quot;, cur_column())) == 0, 1, 0)
          }
        }
      ),
      across(
        matches(&quot;^actor[1-6]_bystander$&quot;),
        ~ {
          corresponding_actor &lt;- get(gsub(&quot;_bystander&quot;, &quot;&quot;, cur_column()))
          if (is.na(corresponding_actor)) {
            NA_integer_
          } else {
            ifelse(
              . &gt; 0 &amp;
                get(gsub(&quot;_bystander&quot;, &quot;_initiator&quot;, cur_column())) == 0 &amp;
                get(gsub(&quot;_bystander&quot;, &quot;_perpetrator&quot;, cur_column())) == 0 &amp;
                get(gsub(&quot;_bystander&quot;, &quot;_intervener&quot;, cur_column())) == 0, 1, 0
            )
          }
        }
      )
    ) %&gt;%
    ungroup() %&gt;%
    mutate(
      actor1_violence = case_when(actor1_violence == &quot;NA&quot; ~ NA_character_, TRUE ~ actor1_violence),
      actor2_violence = case_when(actor2_violence == &quot;NA&quot; ~ NA_character_, TRUE ~ actor2_violence),
      actor3_violence = case_when(actor3_violence == &quot;NA&quot; ~ NA_character_, TRUE ~ actor3_violence),
      actor4_violence = case_when(actor4_violence == &quot;NA&quot; ~ NA_character_, TRUE ~ actor4_violence),
      actor5_violence = case_when(actor5_violence == &quot;NA&quot; ~ NA_character_, TRUE ~ actor5_violence),
      actor6_violence = case_when(actor6_violence == &quot;NA&quot; ~ NA_character_, TRUE ~ actor6_violence)
    ) %&gt;%
    select(
      event_id:election, date_start, date_end, city, location, latitude, longitude, geo_precision,
      actor1, actor1_id, actor1_type, actor1_subtype, actor1_party, actor1_violence, actor1_initiator, actor1_perpetrator, actor1_intervener, actor1_bystander, actor1_victim,
      actor2, actor2_id, actor2_type, actor2_subtype, actor2_party, actor2_violence, actor2_initiator, actor2_perpetrator, actor2_intervener, actor2_bystander, actor2_victim,
      actor3, actor3_id, actor3_type, actor3_subtype, actor3_party, actor3_violence, actor3_initiator, actor3_perpetrator, actor3_intervener, actor3_bystander, actor3_victim,
      actor4, actor4_id, actor4_type, actor4_subtype, actor4_party, actor4_violence, actor4_initiator, actor4_perpetrator, actor4_intervener, actor4_bystander, actor4_victim,
      actor5, actor5_id, actor5_type, actor5_subtype, actor5_party, actor5_violence, actor5_initiator, actor5_perpetrator, actor5_intervener, actor5_bystander, actor5_victim,
      actor6, actor6_id, actor6_type, actor6_subtype, actor6_party, actor6_violence, actor6_initiator, actor6_perpetrator, actor6_intervener, actor6_bystander, actor6_victim,
      event_context:damage, deaths_best:injuries_high,
      certain, certain1:certain6, source, number_of_sources:aggregation
    )
}

# Aggregate filtered event report data using the most-representative aggregation procedure

maverick_rep_media &lt;- revised_aggregation(maverick_media)

maverick_rep_reports &lt;- revised_aggregation(maverick_reports)

##### Figure D1: Share of actor records by actor subtype, country, and source mix ####

maverick_rep_media &lt;- maverick_rep_media %&gt;% 
  mutate(source_mix = &quot;Media sources&quot;)

maverick_rep_reports &lt;- maverick_rep_reports %&gt;% 
  mutate(source_mix = &quot;Reports&quot;)

maverick_rep_media %&gt;% 
  mutate(source_mix = &quot;Media sources&quot;) %&gt;% 
  add_row(maverick_rep_reports) %&gt;% 
  select(
    event_id, country, actor1_subtype, actor2_subtype, actor3_subtype,
    actor4_subtype, actor5_subtype, actor6_subtype, source_mix
  ) %&gt;% 
  pivot_longer(
    cols = c(-event_id, -country, -source_mix),
    names_to = &quot;actor&quot;,
    values_to = &quot;actor_subtype&quot;
  ) %&gt;% 
  filter(!is.na(actor_subtype)) %&gt;% 
  mutate(
    actor_subtype = case_when(
      actor_subtype == &quot;Other&quot; ~ &quot;Other/Unknown&quot;,
      actor_subtype == &quot;Indeterminate&quot; ~ &quot;Other/Unknown&quot;,
      actor_subtype == &quot;&quot; ~ &quot;Other/Unknown&quot;,
      TRUE ~ actor_subtype
    )
  ) %&gt;% 
  group_by(actor_subtype, country, source_mix) %&gt;%
  summarize(count = n(), .groups = &quot;drop&quot;) %&gt;%
  group_by(country) %&gt;%
  mutate(proportion = count / sum(count)) %&gt;% 
  ungroup() %&gt;%
  ggplot() +
  geom_linerange(
    aes(x = actor_subtype, ymin = 0, ymax = proportion, color = source_mix),
    position = position_dodge(width = 1)
  ) +
  geom_point(
    aes(x = actor_subtype, y = proportion, color = source_mix), 
    size = 3, position = position_dodge(width = 1)
  ) +
  facet_wrap(~ country) +
  geom_text(
    aes(
      x = actor_subtype, y = proportion + 0.02, color = source_mix,
      label = paste0(round(proportion*100,0), &quot;%&quot;)
    ),
    size = 4, position = position_dodge(width = 1)
  ) +
  scale_y_continuous(labels = scales::percent_format(), breaks = seq(0, 0.4, 0.05)) +
  coord_flip() +
  labs(
    x = NULL,
    y = &quot;Share of actor records&quot;
  ) +
  scale_color_manual(name = &quot;Source mix&quot;, values = rev(wes_palette(&quot;Zissou1&quot;, 2, type = &quot;continuous&quot;))) +
  my_theme +
  theme(
    axis.text.y = element_text(size = 16),
    legend.position = &quot;top&quot;,
    legend.justification = c(0, 1),
    legend.box.just = &quot;left&quot;,
    legend.margin = margin(0, 0, 0, 0)
  )</code></pre>
<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
<pre class="r"><code>ggsave(&quot;figure_d1.pdf&quot;, width = 13, height = 10, dpi = 600)

# Make dataset spatial

maverick_rep_media &lt;- maverick_rep_media %&gt;%
  filter(geo_precision &gt; 2 &amp; !is.na(latitude) &amp; !is.na(longitude) &amp; latitude != 0 &amp; longitude != 0) %&gt;% 
  mutate(year = year(as.Date(date_start, format = &quot;%Y-%m-%d&quot;))) %&gt;% 
  mutate(country_year = paste(country, year, sep = &quot;_&quot;)) %&gt;% 
  st_as_sf(coords = c(&quot;longitude&quot;, &quot;latitude&quot;), crs = 4326, remove = FALSE)

# Add district names from shapefiles

maverick_rep_media &lt;- maverick_rep_media %&gt;%
  st_join(shapefile_civ %&gt;% select(ADM1_FR), left = TRUE) %&gt;% 
  st_join(shapefile_ke %&gt;% select(shapeName), left = TRUE) %&gt;% 
  mutate(
    district = case_when(
      country == &quot;Ivory Coast&quot; ~ ADM1_FR,
      TRUE ~ shapeName
    )
  ) %&gt;%
  filter(!is.na(district)) %&gt;% 
  select(-ADM1_FR, -shapeName)

# Join dataset with PRIO-Grid dataset

maverick_rep_media &lt;- maverick_rep_media %&gt;% 
  st_join(shapefile_prio_grid)

# Create variables for analysis 

maverick_rep_media &lt;- maverick_rep_media %&gt;% 
  # Create security force involvement variable
  mutate(
    event_context = case_when(
      event_context == &quot;Canvasing&quot; ~ &quot;Other context&quot;,
      event_context == &quot;Incumbent campaign event&quot; ~ &quot;Campaign event&quot;,
      event_context == &quot;Arrest or raid&quot; ~ &quot;Military operation&quot;,
      event_context == &quot;Opposition campaign event&quot; ~ &quot;Campaign event&quot;,
      event_context == &quot;Riot&quot; ~ &quot;Protest or riot&quot;,
      event_context == &quot;Protest&quot; ~ &quot;Protest or riot&quot;,
      event_context == &quot;Security force crack-down on protest&quot; ~ &quot;Protest or riot&quot;,
      event_context == &quot;Vote counting&quot; ~ &quot;Voting procedures&quot;,
      event_context == &quot;Voting&quot; ~ &quot;Voting procedures&quot;,
      event_context == &quot;Indeterminate&quot; ~ &quot;Unknown&quot;,
      TRUE ~ event_context
    ),
    sf_involve = case_when(
      actor1_type == &quot;Security forces&quot; &amp; actor1_bystander != 1 ~ 1,
      actor2_type == &quot;Security forces&quot; &amp; actor2_bystander != 1 ~ 1,
      actor3_type == &quot;Security forces&quot; &amp; actor3_bystander != 1 ~ 1,
      actor4_type == &quot;Security forces&quot; &amp; actor4_bystander != 1 ~ 1,
      actor5_type == &quot;Security forces&quot; &amp; actor5_bystander != 1 ~ 1,
      actor6_type == &quot;Security forces&quot; &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    ),
    police_involve = case_when(
      actor1_subtype == &quot;Security forces: Police&quot; &amp; actor1_bystander != 1 ~ 1,
      actor2_subtype == &quot;Security forces: Police&quot; &amp; actor2_bystander != 1 ~ 1,
      actor3_subtype == &quot;Security forces: Police&quot; &amp; actor3_bystander != 1 ~ 1,
      actor4_subtype == &quot;Security forces: Police&quot; &amp; actor4_bystander != 1 ~ 1,
      actor5_subtype == &quot;Security forces: Police&quot; &amp; actor5_bystander != 1 ~ 1,
      actor6_subtype == &quot;Security forces: Police&quot; &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    ),
    army_involve = case_when(
      actor1_subtype == &quot;Security forces: Army&quot; &amp; actor1_bystander != 1 ~ 1,
      actor2_subtype == &quot;Security forces: Army&quot; &amp; actor2_bystander != 1 ~ 1,
      actor3_subtype == &quot;Security forces: Army&quot; &amp; actor3_bystander != 1 ~ 1,
      actor4_subtype == &quot;Security forces: Army&quot; &amp; actor4_bystander != 1 ~ 1,
      actor5_subtype == &quot;Security forces: Army&quot; &amp; actor5_bystander != 1 ~ 1,
      actor6_subtype == &quot;Security forces: Army&quot; &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    ),
    ppolice_involve = case_when(
      actor1_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor1_bystander != 1 ~ 1,
      actor2_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor2_bystander != 1 ~ 1,
      actor3_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor3_bystander != 1 ~ 1,
      actor4_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor4_bystander != 1 ~ 1,
      actor5_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor5_bystander != 1 ~ 1,
      actor6_subtype == &quot;Security forces: Paramilitary police&quot; &amp; actor6_bystander != 1 ~ 1,
      TRUE ~ 0
    )
  )

# Convert the variables to factors

maverick_rep_media$election &lt;- as.factor(maverick_rep_media$election)

# Set the reference categories 

maverick_rep_media$election &lt;- relevel(maverick_rep_media$election, ref = &quot;CI-Presidential-1995&quot;)

# Remove events in elections with fewer than 10 violent events or for which the election is unknown

maverick_rep_media &lt;- maverick_rep_media %&gt;% 
  group_by(election) %&gt;% 
  mutate(count = n()) %&gt;% 
  ungroup() %&gt;% 
  filter(count &gt;= 10 &amp; election != &quot;Other&quot; &amp; election != &quot;Indeterminate&quot;) 

##### Model 22 ####

model1 &lt;- glm.nb(deaths_best ~ sf_involve + agri_gc + ttime_mean + election, data = maverick_rep_media, link = &quot;log&quot;)

# Calculate standard errors clustered by district

coeftest(model1, vcov = vcovCL(model1, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                 Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                   -0.6870910   0.6071870  -1.1316 0.2578039    
## sf_involve                    -0.7822152   0.2018054  -3.8761 0.0001061 ***
## agri_gc                        0.0041387   0.0062255   0.6648 0.5061842    
## ttime_mean                     0.0021256   0.0011319   1.8779 0.0604014 .  
## electionCI-Legislative-2001    0.7031255   0.9715316   0.7237 0.4692322    
## electionCI-Municipal-2013    -36.4693901   0.6849310 -53.2453 &lt; 2.2e-16 ***
## electionCI-Municipal-2018     -1.2921670   0.6828702  -1.8923 0.0584565 .  
## electionCI-Presidential-2000   0.8356023   0.6151978   1.3583 0.1743792    
## electionCI-Presidential-2010   2.3995096   0.6414455   3.7408 0.0001834 ***
## electionCI-Presidential-2020  -0.1545370   0.5038081  -0.3067 0.7590430    
## electionK-General-1992        -2.7072190   1.1055875  -2.4487 0.0143385 *  
## electionK-General-1997         1.1584601   0.7985398   1.4507 0.1468570    
## electionK-General-2002         0.0254711   0.7463638   0.0341 0.9727760    
## electionK-General-2007         0.9908621   0.4630723   2.1398 0.0323744 *  
## electionK-General-2013         1.3005825   0.5467755   2.3786 0.0173766 *  
## electionK-General-2017         0.1538395   0.4934452   0.3118 0.7552183    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 23####

model2 &lt;- glm.nb(deaths_best ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_rep_media, link = &quot;log&quot;)

# Calculate standard errors clustered by district

coeftest(model2, vcov = vcovCL(model2, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                 Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                   -1.2041854   0.6318883  -1.9057 0.0566900 .  
## police_involve                -0.9170721   0.1997482  -4.5911 4.408e-06 ***
## ppolice_involve                0.4305824   0.3344966   1.2873 0.1980054    
## army_involve                   1.1872108   0.9943087   1.1940 0.2324755    
## agri_gc                        0.0061716   0.0065902   0.9365 0.3490229    
## ttime_mean                     0.0021637   0.0012949   1.6710 0.0947305 .  
## electionCI-Legislative-2001    0.7773857   0.9135877   0.8509 0.3948164    
## electionCI-Municipal-2013    -36.2927261   0.7686389 -47.2169 &lt; 2.2e-16 ***
## electionCI-Municipal-2018     -1.1050348   0.5768760  -1.9155 0.0554224 .  
## electionCI-Presidential-2000   1.1779754   0.5994008   1.9653 0.0493847 *  
## electionCI-Presidential-2010   2.6095955   0.7783183   3.3529 0.0007998 ***
## electionCI-Presidential-2020   0.1715639   0.5297472   0.3239 0.7460441    
## electionK-General-1992        -2.4281169   1.0832371  -2.2415 0.0249913 *  
## electionK-General-1997         1.1000541   0.6531242   1.6843 0.0921246 .  
## electionK-General-2002         0.4666219   0.7212135   0.6470 0.5176348    
## electionK-General-2007         1.4120733   0.3931082   3.5921 0.0003281 ***
## electionK-General-2013         1.7859897   0.4873835   3.6644 0.0002479 ***
## electionK-General-2017         0.6363720   0.4528731   1.4052 0.1599652    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 24 ####

model3 &lt;- glm.nb(injuries_best ~ sf_involve + agri_gc + ttime_mean + election, data = maverick_rep_media, link = &quot;log&quot;)</code></pre>
<pre><code>## Warning: glm.fit: algorithm did not converge</code></pre>
<pre><code>## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: algorithm did not converge</code></pre>
<pre><code>## Warning in glm.nb(injuries_best ~ sf_involve + agri_gc + ttime_mean + election,
## : alternation limit reached</code></pre>
<pre class="r"><code># Calculate standard errors clustered by district

coeftest(model3, vcov = vcovCL(model3, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                Estimate Std. Error z value  Pr(&gt;|z|)    
## (Intercept)                   1.6687564  0.7356874  2.2683 0.0233112 *  
## sf_involve                   -0.1883571  0.3445156 -0.5467 0.5845641    
## agri_gc                      -0.0177721  0.0056661 -3.1365 0.0017095 ** 
## ttime_mean                    0.0045947  0.0017806  2.5804 0.0098672 ** 
## electionCI-Legislative-2001  -1.3498739  0.9901087 -1.3634 0.1727693    
## electionCI-Municipal-2013    -1.1489821  0.6987370 -1.6444 0.1000998    
## electionCI-Municipal-2018    -1.8994797  0.7661227 -2.4793 0.0131625 *  
## electionCI-Presidential-2000 -3.0538433  0.7922475 -3.8547 0.0001159 ***
## electionCI-Presidential-2010 -0.7181858  0.6224567 -1.1538 0.2485853    
## electionCI-Presidential-2020 -0.6712793  0.7893620 -0.8504 0.3950986    
## electionK-General-1992        0.6094499  1.0638413  0.5729 0.5667282    
## electionK-General-1997       -0.3717022  0.8664050 -0.4290 0.6679111    
## electionK-General-2002       -0.1759148  0.7840844 -0.2244 0.8224796    
## electionK-General-2007       -1.8147505  0.7454407 -2.4345 0.0149138 *  
## electionK-General-2013       -2.5214927  0.8802404 -2.8645 0.0041760 ** 
## electionK-General-2017       -1.3048525  1.0320127 -1.2644 0.2060949    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 25 ####

model4 &lt;- glm.nb(injuries_best ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_rep_media, link = &quot;log&quot;)</code></pre>
<pre><code>## Warning: glm.fit: algorithm did not converge</code></pre>
<pre><code>## Warning: glm.fit: algorithm did not converge</code></pre>
<pre><code>## Warning in glm.nb(injuries_best ~ police_involve + ppolice_involve +
## army_involve + : alternation limit reached</code></pre>
<pre class="r"><code># Calculate standard errors clustered by district

coeftest(model4, vcov = vcovCL(model4, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                Estimate Std. Error z value  Pr(&gt;|z|)    
## (Intercept)                   1.6008166  0.7728703  2.0713 0.0383343 *  
## police_involve               -0.1585947  0.4053992 -0.3912 0.6956448    
## ppolice_involve               0.9656920  0.6614402  1.4600 0.1442945    
## army_involve                 -0.2281939  0.9750447 -0.2340 0.8149584    
## agri_gc                      -0.0180375  0.0054313 -3.3210 0.0008969 ***
## ttime_mean                    0.0048097  0.0017320  2.7770 0.0054863 ** 
## electionCI-Legislative-2001  -1.3045250  1.0140485 -1.2865 0.1982853    
## electionCI-Municipal-2013    -1.2730608  0.7302630 -1.7433 0.0812828 .  
## electionCI-Municipal-2018    -1.8514399  0.7944506 -2.3305 0.0197816 *  
## electionCI-Presidential-2000 -3.1621253  0.8423542 -3.7539 0.0001741 ***
## electionCI-Presidential-2010 -0.6980970  0.6583934 -1.0603 0.2890064    
## electionCI-Presidential-2020 -0.6266681  0.8265931 -0.7581 0.4483709    
## electionK-General-1992       -0.1034870  0.9691534 -0.1068 0.9149628    
## electionK-General-1997       -0.5996397  0.8324990 -0.7203 0.4713472    
## electionK-General-2002       -0.1366744  0.7820227 -0.1748 0.8612601    
## electionK-General-2007       -1.7813287  0.7472458 -2.3839 0.0171322 *  
## electionK-General-2013       -2.6677268  0.8539239 -3.1241 0.0017836 ** 
## electionK-General-2017       -1.3072571  1.1429446 -1.1438 0.2527222    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 26 ####

model5 &lt;- glm(damage ~ sf_involve + agri_gc + ttime_mean + election, data = maverick_rep_media, family = &quot;binomial&quot;)

# Calculate standard errors clustered by district

coeftest(model5, vcov = vcovCL(model5, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                 Estimate  Std. Error z value  Pr(&gt;|z|)    
## (Intercept)                  -1.12891476  0.52683132 -2.1428 0.0321260 *  
## sf_involve                   -0.77024275  0.21038487 -3.6611 0.0002511 ***
## agri_gc                       0.00396118  0.00368618  1.0746 0.2825522    
## ttime_mean                    0.00117601  0.00089243  1.3178 0.1875854    
## electionCI-Legislative-2001   0.03336693  1.35036176  0.0247 0.9802866    
## electionCI-Municipal-2013     0.07422402  0.61258089  0.1212 0.9035595    
## electionCI-Municipal-2018     0.70483794  0.69560978  1.0133 0.3109330    
## electionCI-Presidential-2000 -1.84856695  0.41516360 -4.4526 8.483e-06 ***
## electionCI-Presidential-2010 -1.06128272  0.48977536 -2.1669 0.0302443 *  
## electionCI-Presidential-2020  0.38039303  0.47926330  0.7937 0.4273680    
## electionK-General-1992       -0.58975371  0.79772067 -0.7393 0.4597257    
## electionK-General-1997        0.36503769  0.53142311  0.6869 0.4921420    
## electionK-General-2002       -1.51337995  0.59460519 -2.5452 0.0109220 *  
## electionK-General-2007        0.45581353  0.43816891  1.0403 0.2982149    
## electionK-General-2013       -0.25309244  0.70893367 -0.3570 0.7210885    
## electionK-General-2017       -1.41413618  0.50239862 -2.8148 0.0048812 ** 
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 27 ####

model6 &lt;- glm(damage ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_rep_media, family = &quot;binomial&quot;)

# Calculate standard errors clustered by district

coeftest(model6, vcov = vcovCL(model6, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                 Estimate  Std. Error z value  Pr(&gt;|z|)    
## (Intercept)                  -1.48668482  0.53195296 -2.7948 0.0051937 ** 
## police_involve               -0.69568594  0.23342930 -2.9803 0.0028798 ** 
## ppolice_involve               0.27490079  0.42296261  0.6499 0.5157303    
## army_involve                 -1.37931355  1.43009278 -0.9645 0.3347991    
## agri_gc                       0.00429158  0.00362491  1.1839 0.2364473    
## ttime_mean                    0.00126978  0.00092616  1.3710 0.1703682    
## electionCI-Legislative-2001   0.15916079  1.34681187  0.1182 0.9059282    
## electionCI-Municipal-2013     0.18408934  0.62668289  0.2938 0.7689474    
## electionCI-Municipal-2018     0.91074057  0.71949721  1.2658 0.2055842    
## electionCI-Presidential-2000 -1.64646375  0.47571342 -3.4610 0.0005381 ***
## electionCI-Presidential-2010 -0.94044593  0.51533107 -1.8249 0.0680108 .  
## electionCI-Presidential-2020  0.64103676  0.52092709  1.2306 0.2184841    
## electionK-General-1992       -0.35120228  0.82631980 -0.4250 0.6708222    
## electionK-General-1997        0.63845922  0.52226165  1.2225 0.2215227    
## electionK-General-2002       -1.20482951  0.58694002 -2.0527 0.0400988 *  
## electionK-General-2007        0.74498862  0.45582773  1.6344 0.1021824    
## electionK-General-2013       -0.00293131  0.67962984 -0.0043 0.9965587    
## electionK-General-2017       -1.15026489  0.53214323 -2.1616 0.0306513 *  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code># Create lists of clustered standard errors

cluster_se1 &lt;- sqrt(diag(vcovCL(model1, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se2 &lt;- sqrt(diag(vcovCL(model2, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se3 &lt;- sqrt(diag(vcovCL(model3, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se4 &lt;- sqrt(diag(vcovCL(model4, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se5 &lt;- sqrt(diag(vcovCL(model5, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se6 &lt;- sqrt(diag(vcovCL(model6, type = &quot;HC&quot;, cluster = ~ district)))

# Format additional lines

additional_lines &lt;- list(
  c(&quot;Election fixed effects&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;),
  c(&quot;Aggregation&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;)
)

##### Make regression table ####

stargazer(
  model1,
  model2,
  model3,
  model4,
  model5,
  model6,
  digits = 2,
  single.row = FALSE,
  se = list(cluster_se1, cluster_se2, cluster_se3, cluster_se4, cluster_se5, cluster_se6),
  dep.var.labels = c(&quot;Lethal violence&quot;, &quot;Injurious violence&quot;, &quot;Material destruction&quot;),
  column.labels = c(&quot;(22)&quot;, &quot;(23)&quot;, &quot;(24)&quot;, &quot;(25)&quot;, &quot;(26)&quot;, &quot;(27)&quot;),
  model.names = FALSE,
  model.numbers = FALSE,
  covariate.labels = c(
    &quot;Security force involvement&quot;,
    &quot;Police involvement&quot;,
    &quot;Paramilitary police involvement&quot;,
    &quot;Army involvement&quot;,
    &quot;Agricultural land cover&quot;,
    &quot;Travel time to nearest urban center&quot;
  ),
  omit = c(&quot;election&quot;, &quot;Constant&quot;),
  title = &quot;Using only events sourced media reports&quot;,
  label = &quot;tab:regression-media&quot;,
  table.placement = &quot;t&quot;,
  add.lines = additional_lines,
  font.size = &quot;scriptsize&quot;,
  notes.append = FALSE,
  star.char = c(&quot;+&quot;, &quot;*&quot;, &quot;**&quot;),
  notes = c(
    &quot;The models include only events aggregated based on media event reports.&quot;,
    &quot;Robust standard errors in parentheses, clustered by district/county:&quot;, &quot;+ p $&lt;$ 0.1, * p $&lt;$ 0.05, ** p $&lt;$ 0.01&quot;
  ),
  type = &quot;text&quot;
  # type = &quot;latex&quot;,
  # out = &quot;table_d4.tex&quot;
)</code></pre>
<pre><code>## 
## Using only events sourced media reports
## ================================================================================================================
##                                                                 Dependent variable:                             
##                                     ----------------------------------------------------------------------------
##                                           Lethal violence            Injurious violence      Material destruction
##                                          (22)          (23)          (24)          (25)        (26)      (27)   
## ----------------------------------------------------------------------------------------------------------------
## Security force involvement             -0.78**                       -0.19                    -0.77**           
##                                         (0.20)                      (0.34)                    (0.21)            
##                                                                                                                 
## Police involvement                                    -0.92**                      -0.16                -0.70** 
##                                                       (0.20)                      (0.41)                (0.23)  
##                                                                                                                 
## Paramilitary police involvement                        0.43                        0.97                  0.27   
##                                                       (0.33)                      (0.66)                (0.42)  
##                                                                                                                 
## Army involvement                                       1.19                        -0.23                 -1.38  
##                                                       (0.99)                      (0.98)                (1.43)  
##                                                                                                                 
## Agricultural land cover                 0.004          0.01         -0.02**       -0.02**      0.004     0.004  
##                                         (0.01)        (0.01)        (0.01)        (0.01)      (0.004)   (0.004) 
##                                                                                                                 
## Travel time to nearest urban center     0.002+        0.002+        0.005**       0.005**      0.001     0.001  
##                                        (0.001)        (0.001)       (0.002)       (0.002)     (0.001)   (0.001) 
##                                                                                                                 
## ----------------------------------------------------------------------------------------------------------------
## Election fixed effects                   Yes            Yes           Yes           Yes         Yes       Yes   
## Aggregation                           Most-rep.      Most-rep.     Most-rep.     Most-rep.   Most-rep. Most-rep.
## Observations                             788            788           788           788         788       788   
## Log Likelihood                        -1,090.86      -1,088.69      -854.14       -853.47     -387.69   -389.29 
## theta                               0.22** (0.02)  0.22** (0.02) 0.09** (0.01) 0.09** (0.01)                    
## Akaike Inf. Crit.                      2,213.73      2,213.37      1,740.28      1,742.94     807.38    814.58  
## ================================================================================================================
## Note:                                    The models include only events aggregated based on media event reports.
##                                             Robust standard errors in parentheses, clustered by district/county:
##                                                                               + p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01</code></pre>
<pre class="r"><code>##### Test that the difference in coefficients is statistically significant ####

vcov_cluster &lt;- vcovCL(model2, cluster = ~district)

# Test if police_involve = ppolice_involve

linearHypothesis(model2, &quot;police_involve = ppolice_involve&quot;, vcov. = vcov_cluster)</code></pre>
<pre><code>## 
## Linear hypothesis test:
## police_involve - ppolice_involve = 0
## 
## Model 1: restricted model
## Model 2: deaths_best ~ police_involve + ppolice_involve + army_involve + 
##     agri_gc + ttime_mean + election
## 
## Note: Coefficient covariance matrix supplied.
## 
##   Res.Df Df Chisq Pr(&gt;Chisq)    
## 1    771                        
## 2    770  1 10.97  0.0009257 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code># Test if police_involve = army_involve

linearHypothesis(model2, &quot;police_involve = army_involve&quot;, vcov. = vcov_cluster)</code></pre>
<pre><code>## 
## Linear hypothesis test:
## police_involve - army_involve = 0
## 
## Model 1: restricted model
## Model 2: deaths_best ~ police_involve + ppolice_involve + army_involve + 
##     agri_gc + ttime_mean + election
## 
## Note: Coefficient covariance matrix supplied.
## 
##   Res.Df Df  Chisq Pr(&gt;Chisq)  
## 1    771                       
## 2    770  1 3.5598     0.0592 .
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Clean working environment ####

rm(
  model1, model2, model3, model4, model5, model6,
  cluster_se1, cluster_se2, cluster_se3, cluster_se4, cluster_se5, cluster_se6, vcov_cluster,
  additional_lines, maverick_media, maverick_rep_media, maverick_reports, maverick_rep_reports, revised_aggregation
)

#### Accounting for event context ####

##### Model 28 ####

model1 &lt;- glm.nb(deaths_best ~ sf_involve + agri_gc + ttime_mean + election + event_context, data = maverick_sf, link = &quot;log&quot;)

# Calculate standard errors clustered by district

coeftest(model1, vcov = vcovCL(model1, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                    Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                     -1.0154e+00  5.6252e-01  -1.8051 0.0710548 .  
## sf_involve                      -2.1089e-01  1.6284e-01  -1.2951 0.1952850    
## agri_gc                         -1.7473e-03  4.4035e-03  -0.3968 0.6915135    
## ttime_mean                       2.4034e-03  7.8928e-04   3.0451 0.0023259 ** 
## electionCI-Legislative-2001      1.2244e+00  7.5343e-01   1.6251 0.1041398    
## electionCI-Legislative-2011      1.0999e+00  6.5848e-01   1.6704 0.0948481 .  
## electionCI-Municipal-2013       -3.6281e+01  6.2352e-01 -58.1879 &lt; 2.2e-16 ***
## electionCI-Municipal-2018       -1.0190e+00  5.9536e-01  -1.7116 0.0869613 .  
## electionCI-Presidential-2000     2.0732e+00  5.5865e-01   3.7110 0.0002064 ***
## electionCI-Presidential-2010     1.9374e+00  4.0368e-01   4.7994 1.591e-06 ***
## electionCI-Presidential-2020    -5.5989e-01  4.7338e-01  -1.1828 0.2369068    
## electionK-General-1992           1.6898e+00  7.1575e-01   2.3608 0.0182349 *  
## electionK-General-1997           1.3302e+00  6.5484e-01   2.0313 0.0422227 *  
## electionK-General-2002           1.0228e+00  7.6064e-01   1.3446 0.1787511    
## electionK-General-2007           1.2115e+00  4.8330e-01   2.5067 0.0121877 *  
## electionK-General-2013           2.0357e+00  5.8262e-01   3.4940 0.0004759 ***
## electionK-General-2017          -4.3240e-01  6.1813e-01  -0.6995 0.4842169    
## electionK-General-2022           1.7726e-01  5.4895e-01   0.3229 0.7467694    
## event_contextCampaign event     -2.8128e+00  8.4847e-01  -3.3151 0.0009161 ***
## event_contextClash               6.6655e-01  2.8712e-01   2.3215 0.0202612 *  
## event_contextMilitary operation  5.2554e-01  3.0122e-01   1.7447 0.0810367 .  
## event_contextParty primary      -1.9320e+00  4.6046e-01  -4.1958 2.719e-05 ***
## event_contextProtest or riot    -1.5667e-01  2.9681e-01  -0.5278 0.5976147    
## event_contextUnknown            -2.2423e-02  2.7491e-01  -0.0816 0.9349938    
## event_contextVoting procedures  -1.1961e+00  4.4296e-01  -2.7003 0.0069273 ** 
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 29 ####

model2 &lt;- glm.nb(deaths_best ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election + event_context, data = maverick_sf, link = &quot;log&quot;)

# Calculate standard errors clustered by district

coeftest(model2, vcov = vcovCL(model2, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                    Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                     -1.1745e+00  5.4955e-01  -2.1372 0.0325823 *  
## police_involve                  -3.8329e-01  1.0457e-01  -3.6655 0.0002469 ***
## ppolice_involve                  8.2546e-01  1.6775e-01   4.9209 8.616e-07 ***
## army_involve                     8.4689e-02  8.3711e-01   0.1012 0.9194166    
## agri_gc                         -6.2019e-04  4.3420e-03  -0.1428 0.8864204    
## ttime_mean                       2.4160e-03  7.9845e-04   3.0259 0.0024793 ** 
## electionCI-Legislative-2001      5.6758e-01  7.3368e-01   0.7736 0.4391620    
## electionCI-Legislative-2011      1.1235e+00  6.7206e-01   1.6718 0.0945700 .  
## electionCI-Municipal-2013       -3.6242e+01  6.3285e-01 -57.2684 &lt; 2.2e-16 ***
## electionCI-Municipal-2018       -1.0052e+00  5.7294e-01  -1.7545 0.0793534 .  
## electionCI-Presidential-2000     1.5395e+00  5.7813e-01   2.6629 0.0077476 ** 
## electionCI-Presidential-2010     1.9661e+00  3.9918e-01   4.9253 8.423e-07 ***
## electionCI-Presidential-2020    -5.0516e-01  4.7219e-01  -1.0698 0.2847021    
## electionK-General-1992           1.7488e+00  7.0364e-01   2.4853 0.0129442 *  
## electionK-General-1997           1.2544e+00  6.0463e-01   2.0747 0.0380164 *  
## electionK-General-2002           1.1266e+00  7.7280e-01   1.4578 0.1449074    
## electionK-General-2007           1.3378e+00  4.7559e-01   2.8128 0.0049106 ** 
## electionK-General-2013           2.1755e+00  5.6261e-01   3.8668 0.0001103 ***
## electionK-General-2017          -2.7457e-01  6.2657e-01  -0.4382 0.6612339    
## electionK-General-2022           2.9360e-01  5.4874e-01   0.5350 0.5926217    
## event_contextCampaign event     -2.7038e+00  8.4779e-01  -3.1892 0.0014267 ** 
## event_contextClash               6.9904e-01  2.7284e-01   2.5621 0.0104052 *  
## event_contextMilitary operation  4.4525e-01  3.2519e-01   1.3692 0.1709282    
## event_contextParty primary      -1.9274e+00  4.5727e-01  -4.2150 2.498e-05 ***
## event_contextProtest or riot    -1.9733e-01  2.8228e-01  -0.6991 0.4845185    
## event_contextUnknown            -1.4662e-02  2.7335e-01  -0.0536 0.9572220    
## event_contextVoting procedures  -1.1545e+00  4.4233e-01  -2.6101 0.0090508 ** 
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 30 ####

model3 &lt;- glm.nb(injuries_best ~ sf_involve + agri_gc + ttime_mean + election + event_context, data = maverick_sf, link = &quot;log&quot;)

# Calculate standard errors clustered by district

coeftest(model3, vcov = vcovCL(model3, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                   Estimate Std. Error z value Pr(&gt;|z|)   
## (Intercept)                      1.7799469  1.0718556  1.6606 0.096789 . 
## sf_involve                       0.1714183  0.3136365  0.5466 0.584687   
## agri_gc                         -0.0139833  0.0049858 -2.8046 0.005038 **
## ttime_mean                       0.0020608  0.0013912  1.4812 0.138542   
## electionCI-Legislative-2001     -0.9332726  0.9852630 -0.9472 0.343521   
## electionCI-Legislative-2011     -2.4049665  0.9080864 -2.6484 0.008088 **
## electionCI-Municipal-2013       -1.4441619  0.8310093 -1.7378 0.082239 . 
## electionCI-Municipal-2018       -1.8310009  0.9231710 -1.9834 0.047325 * 
## electionCI-Presidential-2000    -0.7692335  0.9323918 -0.8250 0.409365   
## electionCI-Presidential-2010    -1.2775874  0.9005400 -1.4187 0.155989   
## electionCI-Presidential-2020    -1.9060117  0.8259869 -2.3076 0.021024 * 
## electionK-General-1992          -0.1832851  1.0884186 -0.1684 0.866272   
## electionK-General-1997          -0.2037756  1.0050177 -0.2028 0.839324   
## electionK-General-2002          -0.6626956  0.8790538 -0.7539 0.450925   
## electionK-General-2007          -1.9467489  0.8324321 -2.3386 0.019355 * 
## electionK-General-2013          -0.3393747  1.1144784 -0.3045 0.760736   
## electionK-General-2017          -1.0386235  0.8622533 -1.2045 0.228379   
## electionK-General-2022          -1.9250411  0.8753649 -2.1991 0.027869 * 
## event_contextCampaign event      0.4374413  0.5017223  0.8719 0.383274   
## event_contextClash               0.9349207  0.4185509  2.2337 0.025502 * 
## event_contextMilitary operation -0.4296564  0.4018882 -1.0691 0.285027   
## event_contextParty primary      -0.0592687  0.4220817 -0.1404 0.888328   
## event_contextProtest or riot    -0.6859841  0.4676457 -1.4669 0.142406   
## event_contextUnknown             0.1358474  0.4977222  0.2729 0.784901   
## event_contextVoting procedures  -0.8603053  0.4730716 -1.8186 0.068980 . 
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 31 ####

model4 &lt;- glm.nb(injuries_best ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election + event_context, data = maverick_sf, link = &quot;log&quot;)

# Calculate standard errors clustered by district

coeftest(model4, vcov = vcovCL(model4, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                   Estimate Std. Error z value Pr(&gt;|z|)   
## (Intercept)                      1.8213453  1.0284019  1.7710 0.076553 . 
## police_involve                   0.0871287  0.3182548  0.2738 0.784261   
## ppolice_involve                  0.4182111  0.4792145  0.8727 0.382826   
## army_involve                    -0.2556828  0.5197002 -0.4920 0.622733   
## agri_gc                         -0.0138710  0.0048918 -2.8356 0.004574 **
## ttime_mean                       0.0019928  0.0013791  1.4449 0.148476   
## electionCI-Legislative-2001     -1.1566787  1.0994594 -1.0520 0.292780   
## electionCI-Legislative-2011     -2.4296199  0.9062383 -2.6810 0.007340 **
## electionCI-Municipal-2013       -1.3361763  0.8341594 -1.6018 0.109195   
## electionCI-Municipal-2018       -1.8714440  0.9215893 -2.0307 0.042288 * 
## electionCI-Presidential-2000    -0.7972654  0.9917664 -0.8039 0.421464   
## electionCI-Presidential-2010    -1.2708742  0.9001540 -1.4118 0.157997   
## electionCI-Presidential-2020    -1.9536438  0.8283271 -2.3585 0.018347 * 
## electionK-General-1992          -0.3972553  1.0715426 -0.3707 0.710837   
## electionK-General-1997          -0.3094312  0.9841331 -0.3144 0.753202   
## electionK-General-2002          -0.7174714  0.8654980 -0.8290 0.407122   
## electionK-General-2007          -1.9586391  0.8230251 -2.3798 0.017322 * 
## electionK-General-2013          -0.3826436  1.1168242 -0.3426 0.731886   
## electionK-General-2017          -1.0366067  0.9211313 -1.1254 0.260435   
## electionK-General-2022          -1.9493668  0.8721326 -2.2352 0.025406 * 
## event_contextCampaign event      0.4956635  0.4827461  1.0268 0.304534   
## event_contextClash               0.9468677  0.4000942  2.3666 0.017952 * 
## event_contextMilitary operation -0.4097497  0.4020115 -1.0192 0.308085   
## event_contextParty primary      -0.0300951  0.4129226 -0.0729 0.941899   
## event_contextProtest or riot    -0.6668841  0.4752207 -1.4033 0.160523   
## event_contextUnknown             0.1376334  0.4903865  0.2807 0.778969   
## event_contextVoting procedures  -0.8434810  0.4616376 -1.8271 0.067677 . 
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 32 ####

model5 &lt;- glm(damage ~ sf_involve + agri_gc + ttime_mean + election + event_context, data = maverick_sf, family = &quot;binomial&quot;)

# Calculate standard errors clustered by district

coeftest(model5, vcov = vcovCL(model5, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                    Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                     -1.8163e+00  5.2446e-01  -3.4633 0.0005336 ***
## sf_involve                      -9.2161e-01  1.4192e-01  -6.4941 8.356e-11 ***
## agri_gc                          5.8902e-03  3.1104e-03   1.8937 0.0582652 .  
## ttime_mean                       1.7113e-03  6.1311e-04   2.7911 0.0052523 ** 
## electionCI-Legislative-2001      9.2817e-01  9.2842e-01   0.9997 0.3174437    
## electionCI-Legislative-2011     -8.6277e-01  9.3421e-01  -0.9235 0.3557280    
## electionCI-Municipal-2013        6.7587e-01  5.7064e-01   1.1844 0.2362504    
## electionCI-Municipal-2018        1.0679e+00  6.6942e-01   1.5952 0.1106589    
## electionCI-Presidential-2000    -6.4279e-01  3.7466e-01  -1.7157 0.0862261 .  
## electionCI-Presidential-2010    -3.0370e-01  5.0400e-01  -0.6026 0.5467875    
## electionCI-Presidential-2020     1.7847e+00  5.5151e-01   3.2361 0.0012119 ** 
## electionK-General-1992           1.3962e-01  6.9909e-01   0.1997 0.8417053    
## electionK-General-1997           5.7935e-01  5.4252e-01   1.0679 0.2855753    
## electionK-General-2002          -3.4927e-01  5.8611e-01  -0.5959 0.5512391    
## electionK-General-2007           1.1646e+00  4.2655e-01   2.7302 0.0063300 ** 
## electionK-General-2013          -4.0167e-01  7.8733e-01  -0.5102 0.6099290    
## electionK-General-2017          -3.7969e-01  4.7238e-01  -0.8038 0.4215199    
## electionK-General-2022           3.2335e-01  6.7735e-01   0.4774 0.6331001    
## event_contextCampaign event     -2.0670e-01  5.1438e-01  -0.4019 0.6877914    
## event_contextClash              -3.5525e-01  3.3104e-01  -1.0731 0.2832188    
## event_contextMilitary operation  1.8311e-01  4.3623e-01   0.4198 0.6746605    
## event_contextParty primary      -1.4408e+01  5.4120e-01 -26.6232 &lt; 2.2e-16 ***
## event_contextProtest or riot     3.9481e-01  2.5141e-01   1.5704 0.1163219    
## event_contextUnknown            -1.0588e-01  2.4704e-01  -0.4286 0.6682138    
## event_contextVoting procedures   7.3428e-01  3.7898e-01   1.9375 0.0526857 .  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 33 ####

model6 &lt;- glm(damage ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election + event_context, data = maverick_sf, family = &quot;binomial&quot;)

# Calculate standard errors clustered by district

coeftest(model6, vcov = vcovCL(model6, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                    Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                     -2.1527e+00  5.3749e-01  -4.0051 6.198e-05 ***
## police_involve                  -8.8440e-01  1.4408e-01  -6.1382 8.348e-10 ***
## ppolice_involve                 -1.1758e-01  2.9324e-01  -0.4010 0.6884481    
## army_involve                    -1.8065e+00  1.3445e+00  -1.3436 0.1790740    
## agri_gc                          6.0938e-03  3.0940e-03   1.9695 0.0488913 *  
## ttime_mean                       1.7987e-03  6.2264e-04   2.8888 0.0038675 ** 
## electionCI-Legislative-2001      9.1104e-01  1.0224e+00   0.8911 0.3728858    
## electionCI-Legislative-2011     -5.6766e-01  9.2165e-01  -0.6159 0.5379481    
## electionCI-Municipal-2013        7.3404e-01  5.7293e-01   1.2812 0.2001237    
## electionCI-Municipal-2018        1.2355e+00  6.8474e-01   1.8044 0.0711698 .  
## electionCI-Presidential-2000    -5.1893e-01  4.3364e-01  -1.1967 0.2314288    
## electionCI-Presidential-2010    -1.1705e-01  5.2243e-01  -0.2240 0.8227258    
## electionCI-Presidential-2020     2.0602e+00  5.5453e-01   3.7153 0.0002030 ***
## electionK-General-1992           4.0942e-01  7.0459e-01   0.5811 0.5611864    
## electionK-General-1997           9.0540e-01  5.6375e-01   1.6060 0.1082685    
## electionK-General-2002          -4.6680e-02  5.9358e-01  -0.0786 0.9373179    
## electionK-General-2007           1.4863e+00  4.3093e-01   3.4490 0.0005626 ***
## electionK-General-2013          -1.1703e-01  8.0601e-01  -0.1452 0.8845540    
## electionK-General-2017          -1.1237e-01  4.7605e-01  -0.2361 0.8133890    
## electionK-General-2022           6.2212e-01  6.9190e-01   0.8991 0.3685759    
## event_contextCampaign event     -2.0691e-01  5.1985e-01  -0.3980 0.6906090    
## event_contextClash              -3.8001e-01  3.2780e-01  -1.1593 0.2463506    
## event_contextMilitary operation  1.2597e-01  4.4441e-01   0.2835 0.7768271    
## event_contextParty primary      -1.4416e+01  5.3664e-01 -26.8636 &lt; 2.2e-16 ***
## event_contextProtest or riot     3.2761e-01  2.5660e-01   1.2767 0.2017001    
## event_contextUnknown            -1.1189e-01  2.4657e-01  -0.4538 0.6499682    
## event_contextVoting procedures   7.7870e-01  3.8501e-01   2.0226 0.0431181 *  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code># Create lists of clustered standard errors

cluster_se1 &lt;- sqrt(diag(vcovCL(model1, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se2 &lt;- sqrt(diag(vcovCL(model2, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se3 &lt;- sqrt(diag(vcovCL(model3, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se4 &lt;- sqrt(diag(vcovCL(model4, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se5 &lt;- sqrt(diag(vcovCL(model5, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se6 &lt;- sqrt(diag(vcovCL(model6, type = &quot;HC&quot;, cluster = ~ district)))

# Format additional lines

additional_lines &lt;- list(
  c(&quot;Election fixed effects&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;),
  c(&quot;Event context fixed effects&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;),
  c(&quot;Aggregation&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;)
)

##### Make regression table ####

stargazer(
  model1,
  model2,
  model3,
  model4,
  model5,
  model6,
  digits = 2,
  single.row = FALSE,
  se = list(cluster_se1, cluster_se2, cluster_se3, cluster_se4, cluster_se5, cluster_se6),
  dep.var.labels = c(&quot;Lethal violence&quot;, &quot;Injurious violence&quot;, &quot;Material destruction&quot;),
  column.labels = c(&quot;(28)&quot;, &quot;(29)&quot;, &quot;(30)&quot;, &quot;(31)&quot;, &quot;(32)&quot;, &quot;(33)&quot;),
  model.names = FALSE,
  model.numbers = FALSE,
  covariate.labels = c(
    &quot;Security force involvement&quot;,
    &quot;Police involvement&quot;,
    &quot;Paramilitary police involvement&quot;,
    &quot;Army involvement&quot;,
    &quot;Agricultural land cover&quot;,
    &quot;Travel time to nearest urban center&quot;
  ),
  omit = c(&quot;election&quot;, &quot;event_context&quot;, &quot;Constant&quot;),
  title = &quot;Accounting for event context&quot;,
  label = &quot;tab:regression-context&quot;,
  table.placement = &quot;t&quot;,
  add.lines = additional_lines,
  font.size = &quot;scriptsize&quot;,
  notes.append = FALSE,
  star.char = c(&quot;+&quot;, &quot;*&quot;, &quot;**&quot;),
  notes = c(&quot;Robust standard errors in parentheses, clustered by district/county:&quot;, &quot;+ p $&lt;$ 0.1, * p $&lt;$ 0.05, ** p $&lt;$ 0.01&quot;),
  type = &quot;text&quot;
  # type = &quot;latex&quot;,
  # out = &quot;table_e1.tex&quot;
)</code></pre>
<pre><code>## 
## Accounting for event context
## ===============================================================================================================
##                                                                 Dependent variable:                            
##                                     ---------------------------------------------------------------------------
##                                           Lethal violence           Injurious violence      Material destruction
##                                         (28)          (29)          (30)          (31)        (32)      (33)   
## ---------------------------------------------------------------------------------------------------------------
## Security force involvement              -0.21                       0.17                     -0.92**           
##                                        (0.16)                      (0.31)                    (0.14)            
##                                                                                                                
## Police involvement                                   -0.38**                      0.09                 -0.88** 
##                                                      (0.10)                      (0.32)                (0.14)  
##                                                                                                                
## Paramilitary police involvement                      0.83**                       0.42                  -0.12  
##                                                      (0.17)                      (0.48)                (0.29)  
##                                                                                                                
## Army involvement                                      0.08                        -0.26                 -1.81  
##                                                      (0.84)                      (0.52)                (1.34)  
##                                                                                                                
## Agricultural land cover                -0.002        -0.001        -0.01**       -0.01**      0.01+     0.01*  
##                                        (0.004)       (0.004)       (0.005)       (0.005)     (0.003)   (0.003) 
##                                                                                                                
## Travel time to nearest urban center    0.002**       0.002**        0.002         0.002      0.002**   0.002** 
##                                        (0.001)       (0.001)       (0.001)       (0.001)     (0.001)   (0.001) 
##                                                                                                                
## ---------------------------------------------------------------------------------------------------------------
## Election fixed effects                   Yes           Yes           Yes           Yes         Yes       Yes   
## Event context fixed effects              Yes           Yes           Yes           Yes         Yes       Yes   
## Aggregation                           Most-rep.     Most-rep.     Most-rep.     Most-rep.   Most-rep. Most-rep.
## Observations                            1,762         1,762         1,762         1,762       1,762     1,762  
## Log Likelihood                        -2,090.34     -2,082.96     -2,315.35     -2,314.97    -815.59   -817.35 
## theta                               0.26** (0.02) 0.27** (0.02) 0.25** (0.01) 0.25** (0.01)                    
## Akaike Inf. Crit.                     4,230.68      4,219.91      4,680.71      4,683.93    1,681.18  1,688.69 
## ===============================================================================================================
## Note:                                      Robust standard errors in parentheses, clustered by district/county:
##                                                                              + p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01</code></pre>
<pre class="r"><code>##### Test that the difference in coefficients is statistically significant ####

vcov_cluster &lt;- vcovCL(model2, cluster = ~district)

# Test if police_involve = ppolice_involve

linearHypothesis(model2, &quot;police_involve = ppolice_involve&quot;, vcov. = vcov_cluster)</code></pre>
<pre><code>## 
## Linear hypothesis test:
## police_involve - ppolice_involve = 0
## 
## Model 1: restricted model
## Model 2: deaths_best ~ police_involve + ppolice_involve + army_involve + 
##     agri_gc + ttime_mean + election + event_context
## 
## Note: Coefficient covariance matrix supplied.
## 
##   Res.Df Df  Chisq Pr(&gt;Chisq)    
## 1   1736                         
## 2   1735  1 82.814  &lt; 2.2e-16 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code># Test if police_involve = army_involve

linearHypothesis(model2, &quot;police_involve = army_involve&quot;, vcov. = vcov_cluster)</code></pre>
<pre><code>## 
## Linear hypothesis test:
## police_involve - army_involve = 0
## 
## Model 1: restricted model
## Model 2: deaths_best ~ police_involve + ppolice_involve + army_involve + 
##     agri_gc + ttime_mean + election + event_context
## 
## Note: Coefficient covariance matrix supplied.
## 
##   Res.Df Df  Chisq Pr(&gt;Chisq)
## 1   1736                     
## 2   1735  1 0.3194     0.5719</code></pre>
<pre class="r"><code>##### Clean working environment ####

rm(
  model1, model2, model3, model4, model5, model6,
  cluster_se1, cluster_se2, cluster_se3, cluster_se4, cluster_se5, cluster_se6, vcov_cluster,
  additional_lines
)

#### Using Conley standard errors ####

##### Model 34 ####

model1 &lt;- fenegbin(deaths_best ~ sf_involve + agri_gc + ttime_mean | election, data = maverick_sf)</code></pre>
<pre><code>## NOTE: 1 fixed-effect (15 observations) removed because of only 0 outcomes.</code></pre>
<pre class="r"><code># Calculate Conley standard errors

conley_se1 &lt;- vcov_conley(model1, lat = ~latitude, lon = ~longitude, cutoff = 50)

summary(model1, vcov = conley_se1)</code></pre>
<pre><code>## ML estimation, family = Negative Binomial, Dep. Var.: deaths_best
## Observations: 1,765
## Fixed-effects: election: 14
## Standard-errors: Custom 
##             Estimate Std. Error   z value Pr(&gt;|z|)    
## sf_involve -0.219517   0.157359 -1.395014 0.163012    
## agri_gc    -0.003989   0.004574 -0.872082 0.383164    
## ttime_mean  0.002423   0.000787  3.078705 0.002079 ** 
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## Over-dispersion parameter: theta = 0.2340702  
## Log-Likelihood: -2,127.9   Adj. Pseudo R2: 0.066891
##            BIC:  4,382.8     Squared Cor.: 0.076514</code></pre>
<pre class="r"><code>##### Model 35 ####

model2 &lt;- fenegbin(deaths_best ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean | election, data = maverick_sf)</code></pre>
<pre><code>## NOTE: 1 fixed-effect (15 observations) removed because of only 0 outcomes.</code></pre>
<pre class="r"><code># Calculate Conley standard errors

conley_se2 &lt;- vcov_conley(model2, lat = ~latitude, lon = ~longitude, cutoff = 50)

summary(model2, vcov = conley_se2)</code></pre>
<pre><code>## ML estimation, family = Negative Binomial, Dep. Var.: deaths_best
## Observations: 1,765
## Fixed-effects: election: 14
## Standard-errors: Custom 
##                  Estimate Std. Error   z value   Pr(&gt;|z|)    
## police_involve  -0.407457   0.105088 -3.877296 1.0562e-04 ***
## ppolice_involve  0.809894   0.150635  5.376551 7.5926e-08 ***
## army_involve    -0.024563   0.786558 -0.031228 9.7509e-01    
## agri_gc         -0.002822   0.004422 -0.638147 5.2338e-01    
## ttime_mean       0.002437   0.000786  3.100499 1.9320e-03 ** 
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## Over-dispersion parameter: theta = 0.2392031  
## Log-Likelihood: -2,120.8   Adj. Pseudo R2: 0.069087
##            BIC:  4,383.7     Squared Cor.: 0.067691</code></pre>
<pre class="r"><code>##### Model 36 ####

model3 &lt;- fenegbin(injuries_best ~ sf_involve + agri_gc + ttime_mean | election, data = maverick_sf)

# Calculate Conley standard errors

conley_se3 &lt;- vcov_conley(model3, lat = ~latitude, lon = ~longitude, cutoff = 50)

summary(model3, vcov = conley_se3)</code></pre>
<pre><code>## ML estimation, family = Negative Binomial, Dep. Var.: injuries_best
## Observations: 1,780
## Fixed-effects: election: 15
## Standard-errors: Custom 
##             Estimate Std. Error   z value  Pr(&gt;|z|)    
## sf_involve -0.070456   0.206472 -0.341239 0.7329235    
## agri_gc    -0.013223   0.004532 -2.918007 0.0035228 ** 
## ttime_mean  0.001921   0.001410  1.362264 0.1731144    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## Over-dispersion parameter: theta = 0.2225275  
## Log-Likelihood: -2,421.7   Adj. Pseudo R2: 0.02897 
##            BIC:  4,978.1     Squared Cor.: 0.026229</code></pre>
<pre class="r"><code>##### Model 37 ####

model4 &lt;- fenegbin(injuries_best ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean | election, data = maverick_sf)

# Calculate Conley standard errors

conley_se4 &lt;- vcov_conley(model4, lat = ~latitude, lon = ~longitude, cutoff = 50)

summary(model4, vcov = conley_se4)</code></pre>
<pre><code>## ML estimation, family = Negative Binomial, Dep. Var.: injuries_best
## Observations: 1,780
## Fixed-effects: election: 15
## Standard-errors: Custom 
##                  Estimate Std. Error   z value  Pr(&gt;|z|)    
## police_involve  -0.132941   0.214587 -0.619520 0.5355739    
## ppolice_involve  0.273233   0.355790  0.767962 0.4425097    
## army_involve    -0.206643   0.472846 -0.437020 0.6620972    
## agri_gc         -0.013119   0.004438 -2.955998 0.0031166 ** 
## ttime_mean       0.001882   0.001408  1.336114 0.1815122    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## Over-dispersion parameter: theta = 0.2231595  
## Log-Likelihood: -2,420.8   Adj. Pseudo R2: 0.028537
##            BIC:  4,991.3     Squared Cor.: 0.02751</code></pre>
<pre class="r"><code>##### Model 38 ####

model5 &lt;- feglm(damage ~ sf_involve + agri_gc + ttime_mean | election, data = maverick_sf, family = &quot;binomial&quot;)

# Calculate Conley standard errors

conley_se5 &lt;- vcov_conley(model5, lat = ~latitude, lon = ~longitude, cutoff = 50)

summary(model5, vcov = conley_se5)</code></pre>
<pre><code>## GLM estimation, family = binomial, Dep. Var.: damage
## Observations: 1,780
## Fixed-effects: election: 15
## Standard-errors: Custom 
##             Estimate Std. Error  z value   Pr(&gt;|z|)    
## sf_involve -0.821736   0.125626 -6.54114 6.1051e-11 ***
## agri_gc     0.005745   0.003148  1.82464 6.8055e-02 .  
## ttime_mean  0.001587   0.000572  2.77597 5.5037e-03 ** 
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## Log-Likelihood:  -835.4   Adj. Pseudo R2: 0.158468
##            BIC: 1,805.4     Squared Cor.: 0.204947</code></pre>
<pre class="r"><code>##### Model 39 ####

model6 &lt;- feglm(damage ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean | election, data = maverick_sf, family = &quot;binomial&quot;)

# Calculate Conley standard errors

conley_se6 &lt;- vcov_conley(model6, lat = ~latitude, lon = ~longitude, cutoff = 50)

summary(model6, vcov = conley_se6)</code></pre>
<pre><code>## GLM estimation, family = binomial, Dep. Var.: damage
## Observations: 1,780
## Fixed-effects: election: 15
## Standard-errors: Custom 
##                  Estimate Std. Error   z value  Pr(&gt;|z|)    
## police_involve  -0.782209   0.081146 -9.639541 &lt; 2.2e-16 ***
## ppolice_involve -0.185052   0.358054 -0.516827 0.6052769    
## army_involve    -1.804575   1.204171 -1.498604 0.1339764    
## agri_gc          0.005917   0.003085  1.917733 0.0551449 .  
## ttime_mean       0.001699   0.000584  2.911105 0.0036015 ** 
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
## Log-Likelihood:  -837.3   Adj. Pseudo R2: 0.154608
##            BIC: 1,824.2     Squared Cor.: 0.202595</code></pre>
<pre class="r"><code># Rename co-variates 

dict &lt;- c(
  sf_involve = &quot;Security force involvement&quot;,
  police_involve = &quot;Police involvement&quot;,
  ppolice_involve = &quot;Paramililitary police involvement&quot;,
  army_involve = &quot;Army involvement&quot;,
  agri_gc = &quot;Agricultural land cover&quot;,
  ttime_mean = &quot;Travel time to nearest urban center&quot;,
  deaths_best = &quot;Lethal violence&quot;,
  injuries_best = &quot;Injurious violence&quot;,
  damage = &quot;Material destruction&quot;,
  election = &quot;Election&quot;
)

# Collect the Conley standard errors 

vcov_list &lt;- list(
  vcov_conley(model1, lat = ~latitude, lon = ~longitude, cutoff = 100),
  vcov_conley(model2, lat = ~latitude, lon = ~longitude, cutoff = 100),
  vcov_conley(model3, lat = ~latitude, lon = ~longitude, cutoff = 100),
  vcov_conley(model4, lat = ~latitude, lon = ~longitude, cutoff = 100),
  vcov_conley(model5, lat = ~latitude, lon = ~longitude, cutoff = 100),
  vcov_conley(model6, lat = ~latitude, lon = ~longitude, cutoff = 100)
)

##### Make regression table ####

unlink(&quot;table_e2.tex&quot;)

table_tex &lt;- etable(
  model1, model2, model3, model4, model5, model6,
  headers = list(&quot;^Model&quot; = c(&quot;(34)&quot;, &quot;(35)&quot;, &quot;(36)&quot;, &quot;(37)&quot;, &quot;(38)&quot;, &quot;(39)&quot;)),
  dict = dict,
  vcov = vcov_list,
  order = c(
    &quot;Security force involvement&quot;,
    &quot;Police involvement&quot;,
    &quot;Paramililitary police involvement&quot;,
    &quot;Army involvement&quot;,
    &quot;Agricultural land cover&quot;,
    &quot;Travel time to nearest urban center&quot;
  ),
  digits = &quot;r2&quot;,
  digits.stats = &quot;r2&quot;,
  signif.code = c(&quot;**&quot; = 0.01, &quot;*&quot; = 0.05, &quot;+&quot; = 0.10),
  fitstat = c(&quot;n&quot;, &quot;r2&quot;, &quot;aic&quot;),
  placement = &quot;t&quot;,
  family = FALSE,
  title = &quot;Using Conley standard errors&quot;,
  label = &quot;tab:regression-conley&quot;,
  fontsize = &quot;scriptsize&quot;,
  se.below = TRUE,
  tex = TRUE
)

table_tex &lt;- table_tex[!grepl(&quot;^\\s*Model:.*\\\\\\\\&quot;, table_tex)]

writeLines(table_tex, &quot;table_e2.tex&quot;)

##### Clean working environment ####

rm(
  model1, model2, model3, model4, model5, model6,
  dict, vcov_list,
  conley_se1, conley_se2, conley_se3, conley_se4, conley_se5, conley_se6, table_tex
)

#### Using Poisson regression ####

##### Model 40 ####

model1 &lt;- glm(deaths_best ~ sf_involve + agri_gc + ttime_mean + election, data = maverick_sf, family = poisson(link = &quot;log&quot;))

# Calculate standard errors clustered by district

coeftest(model1, vcov = vcovCL(model1, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                 Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                  -1.5416e+00  5.9768e-01  -2.5793 0.0098990 ** 
## sf_involve                   -2.0870e-01  2.4872e-01  -0.8391 0.4014220    
## agri_gc                       2.9736e-03  4.3317e-03   0.6865 0.4924076    
## ttime_mean                    3.6161e-03  9.1763e-04   3.9407 8.124e-05 ***
## electionCI-Legislative-2001   1.0145e+00  8.3714e-01   1.2119 0.2255570    
## electionCI-Legislative-2011   3.5166e-01  6.4256e-01   0.5473 0.5841892    
## electionCI-Municipal-2013    -1.2161e+01  6.6326e-01 -18.3353 &lt; 2.2e-16 ***
## electionCI-Municipal-2018    -7.3468e-01  6.5703e-01  -1.1182 0.2634857    
## electionCI-Presidential-2000  2.1082e+00  5.4451e-01   3.8718 0.0001080 ***
## electionCI-Presidential-2010  2.0738e+00  4.7305e-01   4.3839 1.166e-05 ***
## electionCI-Presidential-2020 -6.9169e-01  6.5236e-01  -1.0603 0.2890104    
## electionK-General-1992        2.6531e+00  7.5606e-01   3.5091 0.0004496 ***
## electionK-General-1997        1.0079e+00  8.1540e-01   1.2361 0.2164111    
## electionK-General-2002        3.1342e-01  7.1757e-01   0.4368 0.6622729    
## electionK-General-2007        1.2401e+00  4.8967e-01   2.5326 0.0113231 *  
## electionK-General-2013        1.6333e+00  7.2208e-01   2.2619 0.0237013 *  
## electionK-General-2017       -5.0921e-01  6.8695e-01  -0.7413 0.4585306    
## electionK-General-2022       -6.2531e-01  7.2591e-01  -0.8614 0.3890132    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 41 ####

model2 &lt;- glm(deaths_best ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_sf, family = poisson(link = &quot;log&quot;))

# Calculate standard errors clustered by district

coeftest(model2, vcov = vcovCL(model2, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                 Estimate  Std. Error  z value  Pr(&gt;|z|)    
## (Intercept)                  -1.6741e+00  5.2138e-01  -3.2109 0.0013232 ** 
## police_involve               -4.8784e-01  1.4431e-01  -3.3804 0.0007237 ***
## ppolice_involve               3.8773e-01  1.6938e-01   2.2892 0.0220706 *  
## army_involve                  1.0551e-01  7.9029e-01   0.1335 0.8937918    
## agri_gc                       3.4926e-03  4.4158e-03   0.7909 0.4289896    
## ttime_mean                    3.6235e-03  9.0328e-04   4.0115 6.032e-05 ***
## electionCI-Legislative-2001   8.7030e-01  7.4791e-01   1.1637 0.2445653    
## electionCI-Legislative-2011   4.3058e-01  6.2207e-01   0.6922 0.4888231    
## electionCI-Municipal-2013    -1.2133e+01  6.7382e-01 -18.0057 &lt; 2.2e-16 ***
## electionCI-Municipal-2018    -6.6558e-01  6.2196e-01  -1.0701 0.2845561    
## electionCI-Presidential-2000  1.9828e+00  5.6617e-01   3.5022 0.0004614 ***
## electionCI-Presidential-2010  2.1298e+00  4.4875e-01   4.7462 2.073e-06 ***
## electionCI-Presidential-2020 -5.9406e-01  6.2896e-01  -0.9445 0.3449043    
## electionK-General-1992        2.7698e+00  7.2109e-01   3.8412 0.0001224 ***
## electionK-General-1997        1.1474e+00  8.1030e-01   1.4160 0.1567662    
## electionK-General-2002        4.6447e-01  7.2035e-01   0.6448 0.5190660    
## electionK-General-2007        1.3871e+00  4.7579e-01   2.9154 0.0035521 ** 
## electionK-General-2013        1.7497e+00  7.1697e-01   2.4404 0.0146696 *  
## electionK-General-2017       -2.7941e-01  7.1965e-01  -0.3883 0.6978287    
## electionK-General-2022       -4.8052e-01  7.2360e-01  -0.6641 0.5066475    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 42 ####

model3 &lt;- glm(injuries_best ~ sf_involve + agri_gc + ttime_mean + election, data = maverick_sf, family = poisson(link = &quot;log&quot;))

# Calculate standard errors clustered by district

coeftest(model3, vcov = vcovCL(model3, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                Estimate Std. Error z value Pr(&gt;|z|)  
## (Intercept)                   1.5649650  1.0387848  1.5065  0.13193  
## sf_involve                    0.0796040  0.4818285  0.1652  0.86878  
## agri_gc                      -0.0135546  0.0070370 -1.9262  0.05408 .
## ttime_mean                    0.0021657  0.0015078  1.4363  0.15091  
## electionCI-Legislative-2001  -0.7595699  1.0948719 -0.6938  0.48784  
## electionCI-Legislative-2011  -2.2500758  0.9721914 -2.3144  0.02064 *
## electionCI-Municipal-2013    -1.1869197  0.9143622 -1.2981  0.19426  
## electionCI-Municipal-2018    -1.9332993  1.0146981 -1.9053  0.05674 .
## electionCI-Presidential-2000  0.9557523  0.9306390  1.0270  0.30443  
## electionCI-Presidential-2010 -0.8514080  0.9235762 -0.9219  0.35660  
## electionCI-Presidential-2020 -1.4532793  1.0027784 -1.4493  0.14727  
## electionK-General-1992        0.0683681  1.1373238  0.0601  0.95207  
## electionK-General-1997       -0.4606914  1.0435660 -0.4415  0.65888  
## electionK-General-2002       -0.4095653  1.0396089 -0.3940  0.69361  
## electionK-General-2007       -1.6629583  0.9141732 -1.8191  0.06890 .
## electionK-General-2013       -1.3255502  1.3906919 -0.9532  0.34051  
## electionK-General-2017       -0.7218849  0.9467501 -0.7625  0.44577  
## electionK-General-2022       -1.4833054  1.1307355 -1.3118  0.18959  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 43 ####

model4 &lt;- glm(injuries_best ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_sf, family = poisson(link = &quot;log&quot;))

# Calculate standard errors clustered by district

coeftest(model4, vcov = vcovCL(model4, cluster = ~ district))</code></pre>
<pre><code>## 
## z test of coefficients:
## 
##                                Estimate Std. Error z value Pr(&gt;|z|)  
## (Intercept)                   1.5633567  0.9245389  1.6910  0.09084 .
## police_involve               -0.0093739  0.5236224 -0.0179  0.98572  
## ppolice_involve               0.3496766  0.4684818  0.7464  0.45542  
## army_involve                 -0.6939508  0.5679320 -1.2219  0.22175  
## agri_gc                      -0.0135684  0.0068422 -1.9830  0.04736 *
## ttime_mean                    0.0021337  0.0015263  1.3980  0.16213  
## electionCI-Legislative-2001  -0.8737202  1.1273612 -0.7750  0.43833  
## electionCI-Legislative-2011  -2.1861329  0.9490610 -2.3035  0.02125 *
## electionCI-Municipal-2013    -1.1548615  0.8970301 -1.2874  0.19795  
## electionCI-Municipal-2018    -1.9096513  0.9807287 -1.9472  0.05151 .
## electionCI-Presidential-2000  0.8708519  0.9892967  0.8803  0.37871  
## electionCI-Presidential-2010 -0.8344884  0.8937955 -0.9336  0.35049  
## electionCI-Presidential-2020 -1.4347998  0.9408529 -1.5250  0.12726  
## electionK-General-1992        0.0951122  1.1004978  0.0864  0.93113  
## electionK-General-1997       -0.4405791  0.9688379 -0.4548  0.64929  
## electionK-General-2002       -0.3810401  0.9538762 -0.3995  0.68955  
## electionK-General-2007       -1.6249421  0.8637236 -1.8813  0.05993 .
## electionK-General-2013       -1.3269523  1.3246323 -1.0018  0.31646  
## electionK-General-2017       -0.6675501  1.0127630 -0.6591  0.50981  
## electionK-General-2022       -1.4493932  1.0520374 -1.3777  0.16830  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code># Create lists of clustered standard errors

cluster_se1 &lt;- sqrt(diag(vcovCL(model1, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se2 &lt;- sqrt(diag(vcovCL(model2, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se3 &lt;- sqrt(diag(vcovCL(model3, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se4 &lt;- sqrt(diag(vcovCL(model4, type = &quot;HC&quot;, cluster = ~ district)))

# Format additional lines

additional_lines &lt;- list(
  c(&quot;Election fixed effects&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;),
  c(&quot;Aggregation&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;)
)

##### Make regression table ####

stargazer(
  model1,
  model2,
  model3,
  model4,
  digits = 2,
  single.row = FALSE,
  se = list(cluster_se1, cluster_se2, cluster_se3, cluster_se4),
  dep.var.labels = c(&quot;Lethal violence&quot;, &quot;Injurious violence&quot;),
  column.labels = c(&quot;(40)&quot;, &quot;(41)&quot;, &quot;(42)&quot;, &quot;(43)&quot;),
  model.names = FALSE,
  model.numbers = FALSE,
  covariate.labels = c(
    &quot;Security force involvement&quot;,
    &quot;Police involvement&quot;,
    &quot;Paramilitary police involvement&quot;,
    &quot;Army involvement&quot;,
    &quot;Agricultural land cover&quot;,
    &quot;Travel time to nearest urban center&quot;
  ),
  omit = c(&quot;election&quot;, &quot;Constant&quot;),
  title = &quot;Using Poisson regression&quot;,
  label = &quot;tab:regression-poisson&quot;,
  table.placement = &quot;t&quot;,
  add.lines = additional_lines,
  font.size = &quot;scriptsize&quot;,
  notes.append = FALSE,
  star.char = c(&quot;+&quot;, &quot;*&quot;, &quot;**&quot;),
  notes = c(&quot;Robust standard errors in parentheses, clustered by district/county:&quot;, &quot;+ p $&lt;$ 0.1, * p $&lt;$ 0.05, ** p $&lt;$ 0.01&quot;),
  type = &quot;text&quot;
  # type = &quot;latex&quot;,
  # out = &quot;table_e3.tex&quot;
)</code></pre>
<pre><code>## 
## Using Poisson regression
## ===========================================================================================================
##                                                               Dependent variable:                          
##                                     -----------------------------------------------------------------------
##                                               Lethal violence                   Injurious violence         
##                                           (40)              (41)              (42)              (43)       
## -----------------------------------------------------------------------------------------------------------
## Security force involvement                -0.21                               0.08                         
##                                          (0.25)                              (0.48)                        
##                                                                                                            
## Police involvement                                         -0.49**                              -0.01      
##                                                            (0.14)                              (0.52)      
##                                                                                                            
## Paramilitary police involvement                             0.39*                               0.35       
##                                                            (0.17)                              (0.47)      
##                                                                                                            
## Army involvement                                            0.11                                -0.69      
##                                                            (0.79)                              (0.57)      
##                                                                                                            
## Agricultural land cover                   0.003             0.003            -0.01+            -0.01*      
##                                          (0.004)           (0.004)           (0.01)            (0.01)      
##                                                                                                            
## Travel time to nearest urban center      0.004**           0.004**            0.002             0.002      
##                                          (0.001)           (0.001)           (0.002)           (0.002)     
##                                                                                                            
## -----------------------------------------------------------------------------------------------------------
## Election fixed effects                     Yes               Yes               Yes               Yes       
## Aggregation                             Most-rep.         Most-rep.         Most-rep.         Most-rep.    
## Observations                              1,780             1,780             1,780             1,780      
## Log Likelihood                          -5,190.65         -5,157.84         -6,838.39         -6,819.48    
## Akaike Inf. Crit.                       10,417.30         10,355.68         13,712.78         13,678.96    
## ===========================================================================================================
## Note:                                  Robust standard errors in parentheses, clustered by district/county:
##                                                                          + p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01</code></pre>
<pre class="r"><code>##### Test that the difference in coefficients is statistically significant ####

vcov_cluster &lt;- vcovCL(model2, cluster = ~district)

# Test if police_involve = ppolice_involve

linearHypothesis(model2, &quot;police_involve = ppolice_involve&quot;, vcov. = vcov_cluster)</code></pre>
<pre><code>## 
## Linear hypothesis test:
## police_involve - ppolice_involve = 0
## 
## Model 1: restricted model
## Model 2: deaths_best ~ police_involve + ppolice_involve + army_involve + 
##     agri_gc + ttime_mean + election
## 
## Note: Coefficient covariance matrix supplied.
## 
##   Res.Df Df  Chisq Pr(&gt;Chisq)    
## 1   1761                         
## 2   1760  1 23.096  1.541e-06 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code># Test if police_involve = army_involve

linearHypothesis(model2, &quot;police_involve = army_involve&quot;, vcov. = vcov_cluster)</code></pre>
<pre><code>## 
## Linear hypothesis test:
## police_involve - army_involve = 0
## 
## Model 1: restricted model
## Model 2: deaths_best ~ police_involve + ppolice_involve + army_involve + 
##     agri_gc + ttime_mean + election
## 
## Note: Coefficient covariance matrix supplied.
## 
##   Res.Df Df  Chisq Pr(&gt;Chisq)
## 1   1761                     
## 2   1760  1 0.5514     0.4577</code></pre>
<pre class="r"><code>##### Clean working environment ####

rm(
  model1, model2, model3, model4,
  cluster_se1, cluster_se2, cluster_se3, cluster_se4, vcov_cluster,
  additional_lines
)

#### Using Linear Probability regression and binary outcome variables ####

# Create binary outcome variables

maverick_sf &lt;- maverick_sf %&gt;% 
  mutate(
    deaths_bin = case_when(deaths_best &gt; 0 ~ 1, TRUE ~ 0),
    injuries_bin = case_when(injuries_best &gt; 0 ~ 1, TRUE ~ 0)
  )

##### Model 44 ####

model1 &lt;- lm(deaths_bin ~ sf_involve + agri_gc + ttime_mean + election, data = maverick_sf)

# Calculate standard errors clustered by district

coeftest(model1, vcov = vcovCL(model1, cluster = ~district))</code></pre>
<pre><code>## 
## t test of coefficients:
## 
##                                 Estimate  Std. Error t value Pr(&gt;|t|)  
## (Intercept)                   0.36701915  0.14898230  2.4635  0.01385 *
## sf_involve                   -0.00777960  0.02004393 -0.3881  0.69797  
## agri_gc                      -0.00072796  0.00057038 -1.2763  0.20202  
## ttime_mean                   -0.00005828  0.00013111 -0.4445  0.65672  
## electionCI-Legislative-2001  -0.08837692  0.21729274 -0.4067  0.68426  
## electionCI-Legislative-2011  -0.07774098  0.17044016 -0.4561  0.64836  
## electionCI-Municipal-2013    -0.33128506  0.14542977 -2.2780  0.02285 *
## electionCI-Municipal-2018    -0.15301474  0.14814200 -1.0329  0.30180  
## electionCI-Presidential-2000 -0.04208999  0.16019137 -0.2627  0.79278  
## electionCI-Presidential-2010  0.27993314  0.15937482  1.7564  0.07919 .
## electionCI-Presidential-2020 -0.10312091  0.12838117 -0.8032  0.42194  
## electionK-General-1992       -0.13461437  0.17358485 -0.7755  0.43815  
## electionK-General-1997       -0.01192588  0.16696416 -0.0714  0.94307  
## electionK-General-2002       -0.08602196  0.14933125 -0.5760  0.56466  
## electionK-General-2007        0.05666846  0.14925502  0.3797  0.70423  
## electionK-General-2013        0.29447372  0.18378580  1.6023  0.10928  
## electionK-General-2017       -0.15336746  0.15116998 -1.0145  0.31047  
## electionK-General-2022       -0.01060614  0.16275880 -0.0652  0.94805  
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 45 ####

model2 &lt;- lm(deaths_bin ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_sf)

# Calculate standard errors clustered by district

coeftest(model2, vcov = vcovCL(model2, cluster = ~district))</code></pre>
<pre><code>## 
## t test of coefficients:
## 
##                                 Estimate  Std. Error t value Pr(&gt;|t|)   
## (Intercept)                   0.34646478  0.15622146  2.2178 0.026697 * 
## police_involve               -0.03932604  0.01665849 -2.3607 0.018348 * 
## ppolice_involve               0.15402554  0.04940699  3.1175 0.001854 **
## army_involve                  0.00225746  0.11529124  0.0196 0.984380   
## agri_gc                      -0.00065950  0.00056455 -1.1682 0.242893   
## ttime_mean                   -0.00006871  0.00013455 -0.5107 0.609647   
## electionCI-Legislative-2001  -0.15002915  0.20968351 -0.7155 0.474393   
## electionCI-Legislative-2011  -0.06075003  0.17263077 -0.3519 0.724950   
## electionCI-Municipal-2013    -0.31479899  0.15088968 -2.0863 0.037096 * 
## electionCI-Municipal-2018    -0.13574296  0.15199147 -0.8931 0.371928   
## electionCI-Presidential-2000 -0.08734673  0.17138531 -0.5097 0.610360   
## electionCI-Presidential-2010  0.28975415  0.16312117  1.7763 0.075854 . 
## electionCI-Presidential-2020 -0.08545092  0.13367921 -0.6392 0.522761   
## electionK-General-1992       -0.11306654  0.17845496 -0.6336 0.526433   
## electionK-General-1997        0.00588427  0.17322599  0.0340 0.972906   
## electionK-General-2002       -0.06158224  0.15559159 -0.3958 0.692305   
## electionK-General-2007        0.08280661  0.15519389  0.5336 0.593707   
## electionK-General-2013        0.31341261  0.18999195  1.6496 0.099201 . 
## electionK-General-2017       -0.11924881  0.16030910 -0.7439 0.457056   
## electionK-General-2022        0.01711810  0.16818476  0.1018 0.918942   
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 46 ####

model3 &lt;- lm(injuries_bin ~ sf_involve + agri_gc + ttime_mean + election, data = maverick_sf)

# Calculate standard errors clustered by district

coeftest(model3, vcov = vcovCL(model3, cluster = ~district))</code></pre>
<pre><code>## 
## t test of coefficients:
## 
##                                 Estimate  Std. Error t value Pr(&gt;|t|)   
## (Intercept)                   2.3998e-01  1.1302e-01  2.1233 0.033864 * 
## sf_involve                    3.9091e-02  3.8441e-02  1.0169 0.309329   
## agri_gc                       1.2671e-03  5.5411e-04  2.2868 0.022327 * 
## ttime_mean                   -6.7986e-05  1.1907e-04 -0.5710 0.568104   
## electionCI-Legislative-2001   1.2391e-01  1.3301e-01  0.9316 0.351687   
## electionCI-Legislative-2011  -1.6167e-01  1.3405e-01 -1.2060 0.227966   
## electionCI-Municipal-2013    -1.5988e-01  1.3013e-01 -1.2286 0.219374   
## electionCI-Municipal-2018    -1.0005e-02  1.3160e-01 -0.0760 0.939409   
## electionCI-Presidential-2000  5.1123e-02  1.1995e-01  0.4262 0.670020   
## electionCI-Presidential-2010 -2.6706e-02  1.1311e-01 -0.2361 0.813383   
## electionCI-Presidential-2020 -1.5176e-01  1.1461e-01 -1.3241 0.185634   
## electionK-General-1992       -9.1485e-02  1.2761e-01 -0.7169 0.473506   
## electionK-General-1997        1.7030e-02  1.2364e-01  0.1377 0.890464   
## electionK-General-2002        2.8487e-01  1.3140e-01  2.1680 0.030293 * 
## electionK-General-2007       -1.4466e-01  1.1093e-01 -1.3041 0.192368   
## electionK-General-2013        7.3863e-02  1.5103e-01  0.4891 0.624850   
## electionK-General-2017        3.3485e-01  1.1270e-01  2.9710 0.003008 **
## electionK-General-2022       -1.6105e-02  1.3287e-01 -0.1212 0.903536   
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 47 ####

model4 &lt;- lm(injuries_bin ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_sf)

# Calculate standard errors clustered by district

coeftest(model4, vcov = vcovCL(model4, cluster = ~district))</code></pre>
<pre><code>## 
## t test of coefficients:
## 
##                                 Estimate  Std. Error t value Pr(&gt;|t|)   
## (Intercept)                   2.3827e-01  1.0822e-01  2.2018 0.027810 * 
## police_involve                2.5538e-02  3.7846e-02  0.6748 0.499908   
## ppolice_involve               1.3338e-01  4.1853e-02  3.1870 0.001463 **
## army_involve                  1.7520e-01  1.2337e-01  1.4201 0.155754   
## agri_gc                       1.3249e-03  5.5085e-04  2.4051 0.016271 * 
## ttime_mean                   -7.5245e-05  1.1942e-04 -0.6301 0.528731   
## electionCI-Legislative-2001   7.4943e-02  1.2715e-01  0.5894 0.555654   
## electionCI-Legislative-2011  -1.7304e-01  1.3508e-01 -1.2811 0.200340   
## electionCI-Municipal-2013    -1.4655e-01  1.2855e-01 -1.1400 0.254447   
## electionCI-Municipal-2018    -3.6857e-03  1.2869e-01 -0.0286 0.977155   
## electionCI-Presidential-2000  4.8123e-03  1.1827e-01  0.0407 0.967548   
## electionCI-Presidential-2010 -2.8326e-02  1.1113e-01 -0.2549 0.798839   
## electionCI-Presidential-2020 -1.5273e-01  1.1272e-01 -1.3549 0.175608   
## electionK-General-1992       -8.9737e-02  1.2730e-01 -0.7049 0.480954   
## electionK-General-1997        8.8842e-03  1.2054e-01  0.0737 0.941254   
## electionK-General-2002        2.8736e-01  1.2740e-01  2.2555 0.024226 * 
## electionK-General-2007       -1.4335e-01  1.0902e-01 -1.3149 0.188703   
## electionK-General-2013        7.1623e-02  1.4782e-01  0.4845 0.628069   
## electionK-General-2017        3.3992e-01  1.0982e-01  3.0952 0.001998 **
## electionK-General-2022       -1.2538e-02  1.3378e-01 -0.0937 0.925342   
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 48 ####

model5 &lt;- lm(damage ~ sf_involve + agri_gc + ttime_mean + election, data = maverick_sf)

# Calculate standard errors clustered by district

coeftest(model5, vcov = vcovCL(model5, cluster = ~district))</code></pre>
<pre><code>## 
## t test of coefficients:
## 
##                                 Estimate  Std. Error t value  Pr(&gt;|t|)    
## (Intercept)                   0.17776568  0.06373876  2.7890 0.0053444 ** 
## sf_involve                   -0.12763605  0.02982110 -4.2801 1.969e-05 ***
## agri_gc                       0.00087889  0.00049211  1.7860 0.0742757 .  
## ttime_mean                    0.00027196  0.00010362  2.6246 0.0087511 ** 
## electionCI-Legislative-2001   0.12558186  0.18807577  0.6677 0.5044001    
## electionCI-Legislative-2011  -0.11766180  0.12639604 -0.9309 0.3520339    
## electionCI-Municipal-2013     0.07063114  0.09011153  0.7838 0.4332516    
## electionCI-Municipal-2018     0.21655064  0.15047897  1.4391 0.1503067    
## electionCI-Presidential-2000 -0.09285191  0.05356731 -1.7334 0.0832051 .  
## electionCI-Presidential-2010 -0.08299526  0.05906231 -1.4052 0.1601335    
## electionCI-Presidential-2020  0.37524783  0.09760801  3.8444 0.0001251 ***
## electionK-General-1992       -0.02347007  0.10296266 -0.2279 0.8197136    
## electionK-General-1997        0.07253966  0.08805467  0.8238 0.4101633    
## electionK-General-2002       -0.13533519  0.07340020 -1.8438 0.0653803 .  
## electionK-General-2007        0.19678591  0.06509123  3.0232 0.0025370 ** 
## electionK-General-2013       -0.06377766  0.11380585 -0.5604 0.5752727    
## electionK-General-2017       -0.06417322  0.06455746 -0.9940 0.3203359    
## electionK-General-2022        0.05985563  0.10840326  0.5522 0.5809107    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code>##### Model 49 ####

model6 &lt;- lm(damage ~ police_involve + ppolice_involve + army_involve + agri_gc + ttime_mean + election, data = maverick_sf)

# Calculate standard errors clustered by district

coeftest(model6, vcov = vcovCL(model6, cluster = ~district))</code></pre>
<pre><code>## 
## t test of coefficients:
## 
##                                 Estimate  Std. Error t value  Pr(&gt;|t|)    
## (Intercept)                   0.12526504  0.06473930  1.9349 0.0531608 .  
## police_involve               -0.12242393  0.02306533 -5.3077 1.251e-07 ***
## ppolice_involve              -0.03273590  0.04723077 -0.6931 0.4883348    
## army_involve                 -0.18931361  0.11485648 -1.6483 0.0994773 .  
## agri_gc                       0.00090867  0.00048746  1.8641 0.0624743 .  
## ttime_mean                    0.00028976  0.00010531  2.7515 0.0059928 ** 
## electionCI-Legislative-2001   0.12589579  0.20710368  0.6079 0.5433403    
## electionCI-Legislative-2011  -0.06535785  0.12741524 -0.5130 0.6080496    
## electionCI-Municipal-2013     0.07764025  0.09268771  0.8377 0.4023387    
## electionCI-Municipal-2018     0.24362431  0.15653958  1.5563 0.1198139    
## electionCI-Presidential-2000 -0.07587646  0.06622129 -1.1458 0.2520330    
## electionCI-Presidential-2010 -0.05839672  0.06293530 -0.9279 0.3535944    
## electionCI-Presidential-2020  0.41787739  0.09956655  4.1970 2.840e-05 ***
## electionK-General-1992        0.01612436  0.10655739  0.1513 0.8797400    
## electionK-General-1997        0.12221346  0.08841718  1.3822 0.1670745    
## electionK-General-2002       -0.08904501  0.07396153 -1.2039 0.2287759    
## electionK-General-2007        0.24229459  0.06915502  3.5036 0.0004704 ***
## electionK-General-2013       -0.02116175  0.11092641 -0.1908 0.8487256    
## electionK-General-2017       -0.02386852  0.06618385 -0.3606 0.7184121    
## electionK-General-2022        0.10587563  0.10811418  0.9793 0.3275691    
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code># Create lists of clustered standard errors

cluster_se1 &lt;- sqrt(diag(vcovCL(model1, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se2 &lt;- sqrt(diag(vcovCL(model2, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se3 &lt;- sqrt(diag(vcovCL(model3, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se4 &lt;- sqrt(diag(vcovCL(model4, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se5 &lt;- sqrt(diag(vcovCL(model5, type = &quot;HC&quot;, cluster = ~ district)))
cluster_se6 &lt;- sqrt(diag(vcovCL(model6, type = &quot;HC&quot;, cluster = ~ district)))

# Format additional lines

additional_lines &lt;- list(
  c(&quot;Election fixed effects&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;, &quot;\\text{Yes}&quot;),
  c(&quot;Aggregation&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;, &quot;\\text{Most-rep.}&quot;)
)

##### Make regression table ####

stargazer(
  model1,
  model2,
  model3,
  model4,
  model5,
  model6,
  digits = 2,
  single.row = FALSE,
  se = list(cluster_se1, cluster_se2, cluster_se3, cluster_se4, cluster_se5, cluster_se6),
  dep.var.labels = c(&quot;Lethal violence&quot;, &quot;Injurious violence&quot;, &quot;Material destruction&quot;),
  column.labels = c(&quot;(44)&quot;, &quot;(45)&quot;, &quot;(46)&quot;, &quot;(47)&quot;, &quot;(48)&quot;, &quot;(49)&quot;),
  model.names = FALSE,
  model.numbers = FALSE,
  covariate.labels = c(
    &quot;Security force involvement&quot;,
    &quot;Police involvement&quot;,
    &quot;Paramilitary police involvement&quot;,
    &quot;Army involvement&quot;,
    &quot;Agricultural land cover&quot;,
    &quot;Travel time to nearest urban center&quot;
  ),
  omit = c(&quot;election&quot;, &quot;Constant&quot;),
  title = &quot;Using a Linear Probability Model and binary outcome variables&quot;,
  label = &quot;tab:regression-lpm&quot;,
  table.placement = &quot;t&quot;,
  add.lines = additional_lines,
  font.size = &quot;scriptsize&quot;,
  notes.append = FALSE,
  star.char = c(&quot;+&quot;, &quot;*&quot;, &quot;**&quot;),
  notes = c(&quot;Robust standard errors in parentheses, clustered by district/county:&quot;, &quot;+ p $&lt;$ 0.1, * p $&lt;$ 0.05, ** p $&lt;$ 0.01&quot;),
  type = &quot;text&quot;
  # type = &quot;latex&quot;,
  # out = &quot;table_e4.tex&quot;
)</code></pre>
<pre><code>## 
## Using a Linear Probability Model and binary outcome variables
## ===================================================================================================================================================================================
##                                                                                                   Dependent variable:                                                              
##                                     -----------------------------------------------------------------------------------------------------------------------------------------------
##                                                     Lethal violence                               Injurious violence                             Material destruction              
##                                              (44)                    (45)                    (46)                    (47)                    (48)                    (49)          
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## Security force involvement                   -0.01                                           0.04                                           -0.13**                                
##                                             (0.02)                                          (0.04)                                          (0.03)                                 
##                                                                                                                                                                                    
## Police involvement                                                  -0.04*                                           0.03                                           -0.12**        
##                                                                     (0.02)                                          (0.04)                                          (0.02)         
##                                                                                                                                                                                    
## Paramilitary police involvement                                     0.15**                                          0.13**                                           -0.03         
##                                                                     (0.05)                                          (0.04)                                          (0.05)         
##                                                                                                                                                                                    
## Army involvement                                                     0.002                                           0.18                                           -0.19+         
##                                                                     (0.11)                                          (0.12)                                          (0.11)         
##                                                                                                                                                                                    
## Agricultural land cover                     -0.001                  -0.001                  0.001*                  0.001*                  0.001+                  0.001+         
##                                             (0.001)                 (0.001)                 (0.001)                 (0.001)                (0.0005)                (0.0005)        
##                                                                                                                                                                                    
## Travel time to nearest urban center         -0.0001                 -0.0001                 -0.0001                 -0.0001                0.0003**                0.0003**        
##                                            (0.0001)                (0.0001)                (0.0001)                (0.0001)                (0.0001)                (0.0001)        
##                                                                                                                                                                                    
## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## Election fixed effects                        Yes                     Yes                     Yes                     Yes                     Yes                     Yes          
## Aggregation                                Most-rep.               Most-rep.               Most-rep.               Most-rep.               Most-rep.               Most-rep.       
## Observations                                 1,780                   1,780                   1,780                   1,780                   1,780                   1,780         
## R2                                           0.12                    0.13                    0.20                    0.20                    0.20                    0.20          
## Adjusted R2                                  0.12                    0.12                    0.19                    0.19                    0.19                    0.19          
## Residual Std. Error                    0.44 (df = 1762)        0.43 (df = 1760)        0.44 (df = 1762)        0.43 (df = 1760)        0.39 (df = 1762)        0.39 (df = 1760)    
## F Statistic                         14.61** (df = 17; 1762) 13.84** (df = 19; 1760) 25.70** (df = 17; 1762) 23.50** (df = 19; 1760) 25.60** (df = 17; 1762) 22.59** (df = 19; 1760)
## ===================================================================================================================================================================================
## Note:                                                                                                          Robust standard errors in parentheses, clustered by district/county:
##                                                                                                                                                  + p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01</code></pre>
<pre class="r"><code>##### Test that the difference in coefficients is statistically significant ####

vcov_cluster &lt;- vcovCL(model2, cluster = ~district)

# Test if police_involve = ppolice_involve

linearHypothesis(model2, &quot;police_involve = ppolice_involve&quot;, vcov. = vcov_cluster)</code></pre>
<pre><code>## 
## Linear hypothesis test:
## police_involve - ppolice_involve = 0
## 
## Model 1: restricted model
## Model 2: deaths_bin ~ police_involve + ppolice_involve + army_involve + 
##     agri_gc + ttime_mean + election
## 
## Note: Coefficient covariance matrix supplied.
## 
##   Res.Df Df      F    Pr(&gt;F)    
## 1   1761                        
## 2   1760  1 15.364 9.208e-05 ***
## ---
## Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1</code></pre>
<pre class="r"><code># Test if police_involve = army_involve

linearHypothesis(model2, &quot;police_involve = army_involve&quot;, vcov. = vcov_cluster)</code></pre>
<pre><code>## 
## Linear hypothesis test:
## police_involve - army_involve = 0
## 
## Model 1: restricted model
## Model 2: deaths_bin ~ police_involve + ppolice_involve + army_involve + 
##     agri_gc + ttime_mean + election
## 
## Note: Coefficient covariance matrix supplied.
## 
##   Res.Df Df      F Pr(&gt;F)
## 1   1761                 
## 2   1760  1 0.1222 0.7267</code></pre>
<pre class="r"><code>##### Clean working environment ####

rm(
  model1, model2, model3, model4, model5, model6,
  cluster_se1, cluster_se2, cluster_se3, cluster_se4, cluster_se5, cluster_se6, vcov_cluster,
  additional_lines
)</code></pre>




</div>

<script>

// add bootstrap table styles to pandoc tables
function bootstrapStylePandocTables() {
  $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
}
$(document).ready(function () {
  bootstrapStylePandocTables();
});


</script>

<!-- tabsets -->

<script>
$(document).ready(function () {
  window.buildTabsets("TOC");
});

$(document).ready(function () {
  $('.tabset-dropdown > .nav-tabs > li').click(function () {
    $(this).parent().toggleClass('nav-tabs-open');
  });
});
</script>

<!-- code folding -->


<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
  (function () {
    var script = document.createElement("script");
    script.type = "text/javascript";
    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
    document.getElementsByTagName("head")[0].appendChild(script);
  })();
</script>

</body>
</html>
